Console log: 'Microsoft.ML.Predictor.Tests' from job 4c356b97-e154-4603-ab00-ac5f5ca11d0c workitem 0dda48e3-2153-45ea-b389-311fdc39a6a0 (windows.11.arm64.open) executed on machine a00BKAI running Windows-11-10.0.26100-SP0 C:\h\w\A567095C\w\AD180968\e>set ML_TEST_DATADIR=C:\h\w\A567095C\p C:\h\w\A567095C\w\AD180968\e>set MICROSOFTML_RESOURCE_PATH=C:\h\w\A567095C\w\AD180968\e C:\h\w\A567095C\w\AD180968\e>set PATH=C:\h\w\A567095C\p\dotnet-cli;C:\python3\Scripts\;C:\python3\;C:\Windows\system32;C:\Windows;C:\Windows\System32\Wbem;C:\Windows\System32\WindowsPowerShell\v1.0\;C:\Windows\System32\OpenSSH\;C:\Debuggers\arm64;C:\Users\runner\AppData\Local\Microsoft\WindowsApps C:\h\w\A567095C\w\AD180968\e>call .\runTests.cmd ----- start Tue 08/11/2026 13:17:44.03 =============== To repro directly: ===================================================== pushd C:\h\w\A567095C\w\AD180968\e\ dotnet exec --roll-forward Major --runtimeconfig Microsoft.ML.Predictor.Tests.runtimeconfig.json --depsfile Microsoft.ML.Predictor.Tests.deps.json C:\h\w\A567095C\p/xunit-runner/tools/netcoreapp2.0/xunit.console.dll Microsoft.ML.Predictor.Tests.dll -notrait Category=SkipInCI -xml testResults.xml popd =========================================================================================================== C:\h\w\A567095C\w\AD180968\e>dotnet exec --roll-forward Major --runtimeconfig Microsoft.ML.Predictor.Tests.runtimeconfig.json --depsfile Microsoft.ML.Predictor.Tests.deps.json C:\h\w\A567095C\p/xunit-runner/tools/netcoreapp2.0/xunit.console.dll Microsoft.ML.Predictor.Tests.dll -notrait Category=SkipInCI -xml testResults.xml xUnit.net Console Runner v2.9.3+9712244020 (64-bit .NET 8.0.16) Discovering: Microsoft.ML.Predictor.Tests (method display = ClassAndMethod, method display options = None) Discovered: Microsoft.ML.Predictor.Tests (found 111 test cases) Starting: Microsoft.ML.Predictor.Tests (parallel test collections = on [2 threads], stop on fail = off) Starting test: Microsoft.ML.RunTests.TestTransposer.TransposerTest Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmNoBiasTest Finished test: Microsoft.ML.RunTests.TestTransposer.TransposerTest with memory usage 86,921,216.00 and max memory usage 87,261,184.00 Starting test: Microsoft.ML.RunTests.TestTransposer.TransposerSaverLoaderTest Microsoft.ML.RunTests.TestTransposer.TransposerTest [PASS] Output: Test TransposerTest: aborted: passed Finished test: Microsoft.ML.RunTests.TestTransposer.TransposerSaverLoaderTest with memory usage 102,723,584.00 and max memory usage 104,079,360.00 Starting test: Microsoft.ML.RunTests.CmdLineReverseTests.NewTest Finished test: Microsoft.ML.RunTests.CmdLineReverseTests.NewTest with memory usage 102,961,152.00 and max memory usage 104,079,360.00 Starting test: Microsoft.ML.RunTests.CmdLineReverseTests.ArgumentParseTest Microsoft.ML.RunTests.TestTransposer.TransposerSaverLoaderTest [PASS] Finished test: Microsoft.ML.RunTests.CmdLineReverseTests.ArgumentParseTest with memory usage 103,014,400.00 and max memory usage 104,079,360.00 Starting test: Microsoft.ML.RunTests.TestIniModels.TestGamRegressionIni Output: Wrote row-wise data, schema, and metadata data view in 440926 bytes Wrote A data view in 60607 bytes Wrote B data view in 364868 bytes Wrote C data view in 6491 bytes Wrote D data view in 11587 bytes Wrote E data view in 1036 bytes Wrote F data view in 3875 bytes Test TransposerSaverLoaderTest: completed normally: passed Microsoft.ML.RunTests.TestConcurrency.TestCVWithLRParallel [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestConcurrency.TestBootstrapWithLRParallel [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestResultProcessor.RPProcessClassifierRegressorTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestResultProcessor.RPSingleClassifierTestWithSpace [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestResultProcessor.RPMulticlassifierTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestResultProcessor.RPSingleClassifierTestWIthEmptyLines [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestResultProcessor.RPSingleClassifierTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestBaselines.AAACompareBaselines [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.CmdLine.CmdParsingSingle [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.CmdLine.CmdParsingBasic [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.Global.AssertHandlerTest [SKIP] Disabled Microsoft.ML.RunTests.CmdIndenterTests.TestCmdIndenter [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestParallelFasttreeInterface.CheckFastTreeParallelInterface [SKIP] 'checker' is not a valid value for the 'parag' argument in FastTree Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmNoBiasTest with memory usage 118,718,464.00 and max memory usage 118,718,464.00 Starting test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorLinearSvmTest Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmNoBiasTest [PASS] Output: Running 'LdSvm' on 'breast-cancer' Running as: TrainTest tr=LdSvm{iter=1000 bias=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LdSvm{iter=1000 bias=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 rows with missing feature/label values Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 216 | 23 | 0.9038 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9558 | 0.9497 | OVERALL 0/1 ACCURACY: 0.951684 LOG LOSS/instance: 0.251640 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.730579 AUC: 0.969024 OVERALL RESULTS --------------------------------------- AUC: 0.969024 (0.0000) Accuracy: 0.951684 (0.0000) Positive precision: 0.955752 (0.0000) Positive recall: 0.903766 (0.0000) Negative precision: 0.949672 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.251640 (0.0000) Log-loss reduction: 0.730579 (0.0000) F1 Score: 0.929032 (0.0000) AUPRC: 0.965033 (0.0000) --------------------------------------- Physical memory usage(MB): 108 Virtual memory usage(MB): 2366074 08/11/2026 13:17:52 PM Time elapsed(s): 1.298 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-nob-TrainTest-breast-cancer-out.txt Output matches baseline: 'LdSvm\LDSVM-nob-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-nob-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LdSvm\LDSVM-nob-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-nob-TrainTest-breast-cancer.txt Output matches baseline: 'LdSvm\LDSVM-nob-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 216 | 23 | 0.9038 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9558 | 0.9497 | OVERALL 0/1 ACCURACY: 0.951684 LOG LOSS/instance: 0.251640 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.730579 AUC: 0.969024 OVERALL RESULTS --------------------------------------- AUC: 0.969024 (0.0000) Accuracy: 0.951684 (0.0000) Positive precision: 0.955752 (0.0000) Positive recall: 0.903766 (0.0000) Negative precision: 0.949672 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.251640 (0.0000) Log-loss reduction: 0.730579 (0.0000) F1 Score: 0.929032 (0.0000) AUPRC: 0.965033 (0.0000) --------------------------------------- Physical memory usage(MB): 112 Virtual memory usage(MB): 2366076 08/11/2026 13:17:52 PM Time elapsed(s): 0.085 Suffix of length 34 compared against sequence of length 38 Running 'LdSvm' on 'breast-cancer' Running as: CV tr=LdSvm{iter=1000 bias=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-CV-breast-cancer.txt} threads- maml.exe CV tr=LdSvm{iter=1000 bias=-} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 126 | 8 | 0.9403 negative || 12 | 208 | 0.9455 ||====================== Precision || 0.9130 | 0.9630 | OVERALL 0/1 ACCURACY: 0.943503 LOG LOSS/instance: 0.262484 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.725722 AUC: 0.980801 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 103 | 2 | 0.9810 negative || 36 | 188 | 0.8393 ||====================== Precision || 0.7410 | 0.9895 | OVERALL 0/1 ACCURACY: 0.884498 LOG LOSS/instance: 0.231456 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.743810 AUC: 0.984396 OVERALL RESULTS --------------------------------------- AUC: 0.982598 (0.0018) Accuracy: 0.914001 (0.0295) Positive precision: 0.827025 (0.0860) Positive recall: 0.960625 (0.0203) Negative precision: 0.976218 (0.0133) Negative recall: 0.892370 (0.0531) Log-loss: 0.246970 (0.0155) Log-loss reduction: 0.734766 (0.0090) F1 Score: 0.885366 (0.0411) AUPRC: 0.972638 (0.0101) --------------------------------------- Physical memory usage(MB): 112 Virtual memory usage(MB): 2366079 08/11/2026 13:17:53 PM Time elapsed(s): 0.981 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-nob-CV-breast-cancer-out.txt Output matches baseline: 'LdSvm\LDSVM-nob-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-nob-CV-breast-cancer-rp.txt Output matches baseline: 'LdSvm\LDSVM-nob-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-nob-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-nob-CV-breast-cancer.txt Output matches baseline: 'LdSvm\LDSVM-nob-CV-breast-cancer.txt' Test BinaryClassifierLDSvmNoBiasTest: completed normally: passed Test BinaryClassifierLDSvmNoBiasTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorLinearSvmTest with memory usage 120,279,040.00 and max memory usage 120,279,040.00 Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest Microsoft.ML.RunTests.TestPredictors.PAVCalibratorLinearSvmTest [PASS] Output: Running 'LinearSVM' on 'breast-cancer' Running as: TrainTest tr=LinearSVM{iter=100 lambda=0.03} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt} cali=PAV maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LinearSVM{iter=100 lambda=0.03} cali=PAV dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter) Training calibrator. PAV calibrator: piecewise function approximation has 8 components. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 226 | 13 | 0.9456 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9617 | 0.9710 | OVERALL 0/1 ACCURACY: 0.967789 LOG LOSS/instance: 0.084588 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.909435 AUC: 0.995797 OVERALL RESULTS --------------------------------------- AUC: 0.995797 (0.0000) Accuracy: 0.967789 (0.0000) Positive precision: 0.961702 (0.0000) Positive recall: 0.945607 (0.0000) Negative precision: 0.970982 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.084588 (0.0000) Log-loss reduction: 0.909435 (0.0000) F1 Score: 0.953586 (0.0000) AUPRC: 0.991453 (0.0000) --------------------------------------- Physical memory usage(MB): 114 Virtual memory usage(MB): 2366079 08/11/2026 13:17:53 PM Time elapsed(s): 0.166 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt Output matches baseline: 'LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt Output matches baseline: 'LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt Output matches baseline: 'LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 226 | 13 | 0.9456 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9617 | 0.9710 | OVERALL 0/1 ACCURACY: 0.967789 LOG LOSS/instance: 0.084588 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.909435 AUC: 0.995797 OVERALL RESULTS --------------------------------------- AUC: 0.995797 (0.0000) Accuracy: 0.967789 (0.0000) Positive precision: 0.961702 (0.0000) Positive recall: 0.945607 (0.0000) Negative precision: 0.970982 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.084588 (0.0000) Log-loss reduction: 0.909435 (0.0000) F1 Score: 0.953586 (0.0000) AUPRC: 0.991453 (0.0000) --------------------------------------- Physical memory usage(MB): 114 Virtual memory usage(MB): 2366079 08/11/2026 13:17:53 PM Time elapsed(s): 0.033 Suffix of length 34 compared against sequence of length 39 Running 'LinearSVM' on 'breast-cancer' Running as: CV tr=LinearSVM{iter=100 lambda=0.03} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration.txt} threads- cali=PAV maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali=PAV dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) Training calibrator. PAV calibrator: piecewise function approximation has 6 components. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) Training calibrator. PAV calibrator: piecewise function approximation has 6 components. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 128 | 6 | 0.9552 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9481 | 0.9726 | OVERALL 0/1 ACCURACY: 0.963277 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): -Infinity AUC: 0.994233 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 97 | 8 | 0.9238 negative || 2 | 222 | 0.9911 ||====================== Precision || 0.9798 | 0.9652 | OVERALL 0/1 ACCURACY: 0.969605 LOG LOSS/instance: 0.220291 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.756168 AUC: 0.997491 OVERALL RESULTS --------------------------------------- AUC: 0.995862 (0.0016) Accuracy: 0.966441 (0.0032) Positive precision: 0.963973 (0.0158) Positive recall: 0.939517 (0.0157) Negative precision: 0.968910 (0.0037) Negative recall: 0.979627 (0.0114) Log-loss: Infinity (NaN) Log-loss reduction: -Infinity (NaN) F1 Score: 0.951327 (0.0003) AUPRC: 0.991949 (0.0025) --------------------------------------- Physical memory usage(MB): 114 Virtual memory usage(MB): 2366081 08/11/2026 13:17:53 PM Time elapsed(s): 0.138 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration-out.txt Output matches baseline: 'LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt Output matches baseline: 'LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration.txt Output matches baseline: 'LinearSVM\LinearSVM-CV-breast-cancer.PAVcalibration.txt' Test PAVCalibratorLinearSvmTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.LightGBMPreviousModelBaselineTest [SKIP] LightGBM is 64-bit only Microsoft.ML.RunTests.TestPredictors.RegressorSdcaTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestIniModels.TestGamRegressionIni with memory usage 120,270,848.00 and max memory usage 120,348,672.00 Microsoft.ML.RunTests.TestIniModels.TestGamRegressionIni [PASS] Output: Starting test: Microsoft.ML.RunTests.TestIniModels.TestGamBinaryClassificationIni Test TestGamRegressionIni: aborted: passed Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest with memory usage 122,757,120.00 and max memory usage 122,757,120.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestDiverseSelectorTest Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest [FAIL] Assert.Equal() Failure: Values differ Expected: 0 Actual: 10 Stack Trace: D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs(221,0): at Microsoft.ML.RunTests.BaseTestBaseline.Done() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs(289,0): at Microsoft.ML.RunTests.TestPredictors.BinaryClassifierSymSgdTest() at System.RuntimeMethodHandle.InvokeMethod(Object target, Void** arguments, Signature sig, Boolean isConstructor) at System.Reflection.MethodBaseInvoker.InvokeWithNoArgs(Object obj, BindingFlags invokeAttr) Output: Running 'SymSGD' on 'breast-cancer' Running as: TrainTest tr=SymSGD{nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=SymSGD{nt=1} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Data fully loaded into memory. Initial learning rate is tuned to 100.000000 Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 222 | 17 | 0.9289 negative || 8 | 436 | 0.9820 ||====================== Precision || 0.9652 | 0.9625 | OVERALL 0/1 ACCURACY: 0.963397 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): -Infinity AUC: 0.995533 OVERALL RESULTS --------------------------------------- AUC: 0.995533 (0.0000) Accuracy: 0.963397 (0.0000) Positive precision: 0.965217 (0.0000) Positive recall: 0.928870 (0.0000) Negative precision: 0.962472 (0.0000) Negative recall: 0.981982 (0.0000) Log-loss: Infinity (0.0000) Log-loss reduction: -Infinity (0.0000) F1 Score: 0.946695 (0.0000) AUPRC: 0.990706 (0.0000) --------------------------------------- Physical memory usage(MB): 115 Virtual memory usage(MB): 2366082 08/11/2026 13:17:54 PM Time elapsed(s): 0.331 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\win-x64\SymSGD-TrainTest-breast-cancer-out.txt *** Failure #1: Values to compare are 141 and 222 AllowedVariance: 0.0001 delta: -81 delta2: -81 Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CompareNumbersWithTolerance(Double, Double, System.Nullable`1[System.Int32], Int32, Boolean) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 663 Boolean MatchNumberWithTolerance(System.Text.RegularExpressions.MatchCollection, System.Text.RegularExpressions.MatchCollection, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 615 Boolean GetNumbersFromFile(System.String ByRef, System.String ByRef, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 581 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 556 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 397 Boolean CheckEqualityNormalized(Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\TestCommandBase.cs 79 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 169 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 *** Failure #2: Output and baseline mismatch at line 12, expected ' positive || 141 | 98 | 0.5900' but got ' positive || 222 | 17 | 0.9289' : 'SymSGD\SymSGD-TrainTest-breast-cancer-out.txt' Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 566 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 397 Boolean CheckEqualityNormalized(Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\TestCommandBase.cs 79 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 169 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-summary.txt} Saving predictor summary Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\win-x64\SymSGD-TrainTest-breast-cancer-summary.txt *** Failure #3: Values to compare are -473.82773 and -458.9996 AllowedVariance: 0.0001 delta: -14.8 delta2: -14.8281 Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CompareNumbersWithTolerance(Double, Double, System.Nullable`1[System.Int32], Int32, Boolean) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 663 Boolean MatchNumberWithTolerance(System.Text.RegularExpressions.MatchCollection, System.Text.RegularExpressions.MatchCollection, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 615 Boolean GetNumbersFromFile(System.String ByRef, System.String ByRef, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 581 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 556 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 397 Void b__1(System.String) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 195 Void ForEach(System.Action`1[T]) 0 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 195 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 *** Failure #4: Output and baseline mismatch at line 3, expected '(Bias) -473.82773' but got '(Bias) -458.9996' : 'SymSGD\SymSGD-TrainTest-breast-cancer-summary.txt' Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 566 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 397 Void b__1(System.String) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 195 Void ForEach(System.Action`1[T]) 0 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 195 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\win-x64\SymSGD-TrainTest-breast-cancer-rp.txt *** Failure #5: Values to compare are 0.985535 and 0.995533 AllowedVariance: 0.0001 delta: -0.01 delta2: -0.01 Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CompareNumbersWithTolerance(Double, Double, System.Nullable`1[System.Int32], Int32, Boolean) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 663 Boolean MatchNumberWithTolerance(System.Text.RegularExpressions.MatchCollection, System.Text.RegularExpressions.MatchCollection, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 615 Boolean GetNumbersFromFile(System.String ByRef, System.String ByRef, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 581 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 556 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 397 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 213 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 *** Failure #6: Output and baseline mismatch at line 3, expected '0.985535 0.853587 0.986014 0.589958 0.818519 0.995495 Infinity -Infinity 0.73822 0.975735 1 SymSGD %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=SymSGD{nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /nt:1 ' but got '0.995533 0.963397 0.965217 0.92887 0.962472 0.981982 Infinity -Infinity 0.946695 0.990706 1 SymSGD %Data% %Data% %Output% 99 0 0 maml.exe TrainTest test=%Data% tr=SymSGD{nt=1} norm=No dout=%Output% data=%Data% out=%Output% seed=1 /nt:1 ' : 'SymSGD\SymSGD-TrainTest-breast-cancer-rp.txt' Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 566 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEqualityNormalized(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 397 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 213 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\win-x64\SymSGD-TrainTest-breast-cancer.txt *** Failure #7: Values to compare are -408.12906 and -254.40263 AllowedVariance: 0.0001 delta: -153.7 delta2: -153.7264 Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CompareNumbersWithTolerance(Double, Double, System.Nullable`1[System.Int32], Int32, Boolean) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 663 Boolean MatchNumberWithTolerance(System.Text.RegularExpressions.MatchCollection, System.Text.RegularExpressions.MatchCollection, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 615 Boolean GetNumbersFromFile(System.String ByRef, System.String ByRef, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 581 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 556 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEquality(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 387 Boolean CheckEquality(Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\TestCommandBase.cs 73 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 218 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 *** Failure #8: Output and baseline mismatch at line 2, expected '0 0 -408.12906 0 -0 0' but got '0 0 -254.40263 0 -0 0' : 'SymSGD\SymSGD-TrainTest-breast-cancer.txt' Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 566 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEquality(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 387 Boolean CheckEquality(Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\TestCommandBase.cs 73 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 218 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 362 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 222 | 17 | 0.9289 negative || 8 | 436 | 0.9820 ||====================== Precision || 0.9652 | 0.9625 | OVERALL 0/1 ACCURACY: 0.963397 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): -Infinity AUC: 0.995533 OVERALL RESULTS --------------------------------------- AUC: 0.995533 (0.0000) Accuracy: 0.963397 (0.0000) Positive precision: 0.965217 (0.0000) Positive recall: 0.928870 (0.0000) Negative precision: 0.962472 (0.0000) Negative recall: 0.981982 (0.0000) Log-loss: Infinity (0.0000) Log-loss reduction: -Infinity (0.0000) F1 Score: 0.946695 (0.0000) AUPRC: 0.990706 (0.0000) --------------------------------------- Physical memory usage(MB): 116 Virtual memory usage(MB): 2366084 08/11/2026 13:17:54 PM Time elapsed(s): 0.018 Suffix of length 34 compared against sequence of length 39 Running 'SymSGD' on 'breast-cancer' Running as: CV tr=SymSGD{nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-CV-breast-cancer.txt} norm=no threads- maml.exe CV tr=SymSGD{nt=1} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Data fully loaded into memory. Initial learning rate is tuned to 100.000000 Not training a calibrator because it is not needed. Not adding a normalizer. Data fully loaded into memory. Initial learning rate is tuned to 100.000000 Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 130 | 4 | 0.9701 negative || 8 | 212 | 0.9636 ||====================== Precision || 0.9420 | 0.9815 | OVERALL 0/1 ACCURACY: 0.966102 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): -Infinity AUC: 0.990773 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 96 | 9 | 0.9143 negative || 11 | 213 | 0.9509 ||====================== Precision || 0.8972 | 0.9595 | OVERALL 0/1 ACCURACY: 0.939210 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): -Infinity AUC: 0.963435 OVERALL RESULTS --------------------------------------- AUC: 0.977104 (0.0137) Accuracy: 0.952656 (0.0134) Positive precision: 0.919613 (0.0224) Positive recall: 0.942217 (0.0279) Negative precision: 0.970470 (0.0110) Negative recall: 0.957265 (0.0064) Log-loss: Infinity (NaN) Log-loss reduction: -Infinity (NaN) F1 Score: 0.930771 (0.0251) AUPRC: 0.964044 (0.0164) --------------------------------------- Physical memory usage(MB): 116 Virtual memory usage(MB): 2366085 08/11/2026 13:17:54 PM Time elapsed(s): 0.032 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\win-x64\SymSGD-CV-breast-cancer-out.txt Output matches baseline: 'SymSGD\SymSGD-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\win-x64\SymSGD-CV-breast-cancer-rp.txt Output matches baseline: 'SymSGD\SymSGD-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SymSGD\SymSGD-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SymSGD\netcoreapp\SymSGD-CV-breast-cancer.txt *** Failure #9: Values to compare are 1000.1891 and 994.137 AllowedVariance: 0.0001 delta: 5.9 delta2: 6.0521 Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CompareNumbersWithTolerance(Double, Double, System.Nullable`1[System.Int32], Int32, Boolean) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 663 Boolean MatchNumberWithTolerance(System.Text.RegularExpressions.MatchCollection, System.Text.RegularExpressions.MatchCollection, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 615 Boolean GetNumbersFromFile(System.String ByRef, System.String ByRef, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 581 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 556 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEquality(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 387 Boolean CheckEquality(Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\TestCommandBase.cs 73 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 218 Void Run_CV(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 442 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 363 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 *** Failure #10: Output and baseline mismatch at line 2, expected '5 1 1000.1891 1 -0 1' but got '5 1 994.137 1 -0 1' : 'SymSGD\SymSGD-CV-breast-cancer.txt' Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 566 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Boolean CheckEquality(System.String, System.String, System.String, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 387 Boolean CheckEquality(Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\TestCommandBase.cs 73 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 218 Void Run_CV(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 442 Void RunOneAllTests(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 363 Void BinaryClassifierSymSgdTest() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 288 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCaseRunner.cs 140 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCaseRunner.cs 82 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync(Xunit.Abstractions.IMessageSink, Xunit.Sdk.IMessageBus, System.Object[], Xunit.Sdk.ExceptionAggregator, System.Threading.CancellationTokenSource) /_/src/xunit.execution/Sdk/Frameworks/XunitTestCase.cs 170 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCaseAsync(Xunit.Sdk.IXunitTestCase) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestMethodRunner.cs 45 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCasesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodAsync(Xunit.Abstractions.ITestMethod, Xunit.Abstractions.IReflectionMethodInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Object[]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestClassRunner.cs 206 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 Test BinaryClassifierSymSgdTest: completed normally: failed Test BinaryClassifierSymSgdTest is using netcoreapp configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestDiverseSelectorTest with memory usage 125,378,560.00 and max memory usage 127,156,224.00 Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassLRNonNegativeTest Microsoft.ML.RunTests.TestPredictors.EnsemblesBestDiverseSelectorTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 pt=BestDiverseSelector tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 1 of 20 finished in 00:00:00.1100313 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 2 of 20 finished in 00:00:00.0103565 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 3 of 20 finished in 00:00:00.0020086 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 4 of 20 finished in 00:00:00.0049802 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 20 finished in 00:00:00.0018352 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 6 of 20 finished in 00:00:00.0022644 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 7 of 20 finished in 00:00:00.0018470 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 8 of 20 finished in 00:00:00.0020352 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 9 of 20 finished in 00:00:00.0019243 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 10 of 20 finished in 00:00:00.0018328 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) Trainer 11 of 20 finished in 00:00:00.0018587 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 12 of 20 finished in 00:00:00.0018536 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 13 of 20 finished in 00:00:00.0018263 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 14 of 20 finished in 00:00:00.0017939 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) Trainer 15 of 20 finished in 00:00:00.0017390 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) Trainer 16 of 20 finished in 00:00:00.0017413 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 17 of 20 finished in 00:00:00.0019535 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter) Trainer 18 of 20 finished in 00:00:00.0017746 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 19 of 20 finished in 00:00:00.0032955 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 20 of 20 finished in 00:00:00.0019050 Warning: 10 of 20 trainings failed. Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 236 | 3 | 0.9874 negative || 14 | 430 | 0.9685 ||====================== Precision || 0.9440 | 0.9931 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.114893 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.876989 AUC: 0.996127 OVERALL RESULTS --------------------------------------- AUC: 0.996127 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.944000 (0.0000) Positive recall: 0.987448 (0.0000) Negative precision: 0.993072 (0.0000) Negative recall: 0.968468 (0.0000) Log-loss: 0.114893 (0.0000) Log-loss reduction: 0.876989 (0.0000) F1 Score: 0.965235 (0.0000) AUPRC: 0.992160 (0.0000) --------------------------------------- Physical memory usage(MB): 121 Virtual memory usage(MB): 2366089 08/11/2026 13:17:54 PM Time elapsed(s): 0.506 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Diverse-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Diverse-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Diverse-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Diverse-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 236 | 3 | 0.9874 negative || 14 | 430 | 0.9685 ||====================== Precision || 0.9440 | 0.9931 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.114893 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.876989 AUC: 0.996127 OVERALL RESULTS --------------------------------------- AUC: 0.996127 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.944000 (0.0000) Positive recall: 0.987448 (0.0000) Negative precision: 0.993072 (0.0000) Negative recall: 0.968468 (0.0000) Log-loss: 0.114893 (0.0000) Log-loss reduction: 0.876989 (0.0000) F1 Score: 0.965235 (0.0000) AUPRC: 0.992160 (0.0000) --------------------------------------- Physical memory usage(MB): 121 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.142 Suffix of length 34 compared against sequence of length 119 Test EnsemblesBestDiverseSelectorTest: completed normally: passed Test EnsemblesBestDiverseSelectorTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.DartLightGBMTest [SKIP] LightGBM is 64-bit only Microsoft.ML.RunTests.TestPredictors.RankingTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassLRNonNegativeTest with memory usage 126,787,584.00 and max memory usage 127,156,224.00 Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionGaussianNormTest Microsoft.ML.RunTests.TestPredictors.MulticlassLRNonNegativeTest [PASS] Output: Running 'MulticlassLogisticRegression' on 'iris' Running as: TrainTest tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Not adding a normalizer. Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 48 | 2 | 0.9600 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9600 |0.9600 | Accuracy(micro-avg): 0.973333 Accuracy(macro-avg): 0.973333 Log-loss: 0.099380 Log-loss reduction: 0.909540 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973333 (0.0000) Accuracy(macro-avg): 0.973333 (0.0000) Log-loss: 0.099380 (0.0000) Log-loss reduction: 0.909540 (0.0000) --------------------------------------- Physical memory usage(MB): 121 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.127 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\netcoreapp\LogisticRegression-Non-Negative-TrainTest-iris-out.txt Output matches baseline: 'MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\netcoreapp\LogisticRegression-Non-Negative-TrainTest-iris-rp.txt Output matches baseline: 'MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\netcoreapp\LogisticRegression-Non-Negative-TrainTest-iris.txt Output matches baseline: 'MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 48 | 2 | 0.9600 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9600 |0.9600 | Accuracy(micro-avg): 0.973333 Accuracy(macro-avg): 0.973333 Log-loss: 0.099380 Log-loss reduction: 0.909540 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973333 (0.0000) Accuracy(macro-avg): 0.973333 (0.0000) Log-loss: 0.099380 (0.0000) Log-loss reduction: 0.909540 (0.0000) --------------------------------------- Physical memory usage(MB): 121 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.02 Suffix of length 27 compared against sequence of length 34 Running 'MulticlassLogisticRegression' on 'iris' Running as: CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris.txt} norm=no threads- maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1 nn=+} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} Not adding a normalizer. Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 21 | 0 | 0 | 1.0000 1 || 0 | 29 | 1 | 0.9667 2 || 0 | 2 | 26 | 0.9286 ||======================== Precision ||1.0000 |0.9355 |0.9630 | Accuracy(micro-avg): 0.962025 Accuracy(macro-avg): 0.965079 Log-loss: 0.129885 Log-loss reduction: 0.880567 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 29 | 0 | 0 | 1.0000 1 || 0 | 18 | 2 | 0.9000 2 || 0 | 0 | 22 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.9167 | Accuracy(micro-avg): 0.971831 Accuracy(macro-avg): 0.966667 Log-loss: 0.125565 Log-loss reduction: 0.884341 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.966928 (0.0049) Accuracy(macro-avg): 0.965873 (0.0008) Log-loss: 0.127725 (0.0022) Log-loss reduction: 0.882454 (0.0019) --------------------------------------- Physical memory usage(MB): 121 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.062 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris-out.txt Output matches baseline: 'MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris-rp.txt Output matches baseline: 'MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\netcoreapp\LogisticRegression-Non-Negative-CV-iris.txt Output matches baseline: 'MulticlassLogisticRegression\LogisticRegression-Non-Negative-CV-iris.txt' Test MulticlassLRNonNegativeTest: completed normally: passed Test MulticlassLRNonNegativeTest is using netcoreapp configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionGaussianNormTest with memory usage 128,438,272.00 and max memory usage 130,015,232.00 Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassTreeFeaturizedLRTest Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionGaussianNormTest [PASS] Output: Running 'LogisticRegression' on 'breast-cancer' Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt} xf=MeanVarNormalizer{col=Features} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-model.zip seed=1 xf=MeanVarNormalizer{col=Features} Not adding a normalizer. Warning: Skipped 16 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 229 | 10 | 0.9582 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9582 | 0.9775 | OVERALL 0/1 ACCURACY: 0.970717 LOG LOSS/instance: 0.110699 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.881479 AUC: 0.996231 OVERALL RESULTS --------------------------------------- AUC: 0.996231 (0.0000) Accuracy: 0.970717 (0.0000) Positive precision: 0.958159 (0.0000) Positive recall: 0.958159 (0.0000) Negative precision: 0.977477 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.110699 (0.0000) Log-loss reduction: 0.881479 (0.0000) F1 Score: 0.958159 (0.0000) AUPRC: 0.992209 (0.0000) --------------------------------------- Physical memory usage(MB): 123 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.077 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 229 | 10 | 0.9582 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9582 | 0.9775 | OVERALL 0/1 ACCURACY: 0.970717 LOG LOSS/instance: 0.110699 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.881479 AUC: 0.996231 OVERALL RESULTS --------------------------------------- AUC: 0.996231 (0.0000) Accuracy: 0.970717 (0.0000) Positive precision: 0.958159 (0.0000) Positive recall: 0.958159 (0.0000) Negative precision: 0.977477 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.110699 (0.0000) Log-loss reduction: 0.881479 (0.0000) F1 Score: 0.958159 (0.0000) AUPRC: 0.992209 (0.0000) --------------------------------------- Physical memory usage(MB): 123 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.024 Suffix of length 34 compared against sequence of length 42 Running 'LogisticRegression' on 'breast-cancer' Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer.txt} xf=MeanVarNormalizer{col=Features} threads- maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 xf=MeanVarNormalizer{col=Features} Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 9 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 129 | 5 | 0.9627 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9485 | 0.9771 | OVERALL 0/1 ACCURACY: 0.966102 LOG LOSS/instance: 0.133256 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.860756 AUC: 0.994267 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 96 | 9 | 0.9143 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9697 | 0.9609 | OVERALL 0/1 ACCURACY: 0.963526 LOG LOSS/instance: 0.117262 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.870207 AUC: 0.997449 OVERALL RESULTS --------------------------------------- AUC: 0.995858 (0.0016) Accuracy: 0.964814 (0.0013) Positive precision: 0.959113 (0.0106) Positive recall: 0.938486 (0.0242) Negative precision: 0.968967 (0.0081) Negative recall: 0.977394 (0.0092) Log-loss: 0.125259 (0.0080) Log-loss reduction: 0.865481 (0.0047) F1 Score: 0.948366 (0.0072) AUPRC: 0.991982 (0.0025) --------------------------------------- Physical memory usage(MB): 123 Virtual memory usage(MB): 2366089 08/11/2026 13:17:55 PM Time elapsed(s): 0.061 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-GaussianNorm-CV-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-GaussianNorm-CV-breast-cancer.txt' Test BinaryClassifierLogisticRegressionGaussianNormTest: completed normally: passed Test BinaryClassifierLogisticRegressionGaussianNormTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.FastTreeUnderbuiltRegressionTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestIniModels.TestGamBinaryClassificationIni with memory usage 126,431,232.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestGamPublicInterfaces.TestGamDirectInstantiation Microsoft.ML.RunTests.TestIniModels.TestGamBinaryClassificationIni [PASS] Output: Test TestGamBinaryClassificationIni: aborted: passed Finished test: Microsoft.ML.RunTests.TestGamPublicInterfaces.TestGamDirectInstantiation with memory usage 126,513,152.00 and max memory usage 132,837,376.00 Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassTreeFeaturizedLRTest with memory usage 110,075,904.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.MulticlassTreeFeaturizedLRTest [PASS] Output: Running 'MulticlassLogisticRegression' on 'iris-tree-featurized' Running as: TrainTest tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt} norm=no Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFastRankClassificationTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-model.zip seed=1 xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} Making per-feature arrays Changing data from row-wise to column-wise Processed 150 instances Binning and forming Feature objects Reserved memory for tree learner: 20436 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 72 improvement criterion: Mean Improvement L1 regularization selected 72 of 72 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 49 | 1 | 0.9800 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9608 |0.9796 | Accuracy(micro-avg): 0.980000 Accuracy(macro-avg): 0.980000 Log-loss: 0.048652 Log-loss reduction: 0.955715 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.980000 (0.0000) Accuracy(macro-avg): 0.980000 (0.0000) Log-loss: 0.048652 (0.0000) Log-loss reduction: 0.955715 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:56 PM Time elapsed(s): 1.044 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-out.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-rp.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 49 | 1 | 0.9800 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9608 |0.9796 | Accuracy(micro-avg): 0.980000 Accuracy(macro-avg): 0.980000 Log-loss: 0.048652 Log-loss reduction: 0.955715 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.980000 (0.0000) Accuracy(macro-avg): 0.980000 (0.0000) Log-loss: 0.048652 (0.0000) Log-loss reduction: 0.955715 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:56 PM Time elapsed(s): 0.019 Suffix of length 27 compared against sequence of length 41 Running 'MulticlassLogisticRegression' on 'iris-tree-featurized' Running as: CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized.txt} norm=no threads- maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=TreeFeat{lps=0 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} Making per-feature arrays Changing data from row-wise to column-wise Processed 71 instances Binning and forming Feature objects Reserved memory for tree learner: 16380 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 39 improvement criterion: Mean Improvement L1 regularization selected 39 of 39 weights. Not training a calibrator because it is not needed. Making per-feature arrays Changing data from row-wise to column-wise Processed 79 instances Binning and forming Feature objects Reserved memory for tree learner: 17472 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 54 improvement criterion: Mean Improvement L1 regularization selected 54 of 54 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 21 | 0 | 0 | 1.0000 1 || 0 | 25 | 5 | 0.8333 2 || 0 | 1 | 27 | 0.9643 ||======================== Precision ||1.0000 |0.9615 |0.8438 | Accuracy(micro-avg): 0.924051 Accuracy(macro-avg): 0.932540 Log-loss: 0.330649 Log-loss reduction: 0.695959 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 29 | 0 | 0 | 1.0000 1 || 0 | 19 | 1 | 0.9500 2 || 0 | 2 | 20 | 0.9091 ||======================== Precision ||1.0000 |0.9048 |0.9524 | Accuracy(micro-avg): 0.957746 Accuracy(macro-avg): 0.953030 Log-loss: 0.157832 Log-loss reduction: 0.854619 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.940899 (0.0168) Accuracy(macro-avg): 0.942785 (0.0102) Log-loss: 0.244241 (0.0864) Log-loss reduction: 0.775289 (0.0793) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:56 PM Time elapsed(s): 0.06 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-out.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-rp.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized.txt' Running 'MulticlassLogisticRegression' on 'iris-tree-featurized-permuted' Running as: TrainTest tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-model.zip seed=1 xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} Making per-feature arrays Changing data from row-wise to column-wise Processed 150 instances Binning and forming Feature objects Reserved memory for tree learner: 20436 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 81 improvement criterion: Mean Improvement L1 regularization selected 81 of 81 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 49 | 1 | 0.9800 2 || 0 | 3 | 47 | 0.9400 ||======================== Precision ||1.0000 |0.9423 |0.9792 | Accuracy(micro-avg): 0.973333 Accuracy(macro-avg): 0.973333 Log-loss: 0.052580 Log-loss reduction: 0.952140 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973333 (0.0000) Accuracy(macro-avg): 0.973333 (0.0000) Log-loss: 0.052580 (0.0000) Log-loss reduction: 0.952140 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:56 PM Time elapsed(s): 0.061 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-out.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-rp.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-TrainTest-iris-tree-featurized-permuted-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 49 | 1 | 0.9800 2 || 0 | 3 | 47 | 0.9400 ||======================== Precision ||1.0000 |0.9423 |0.9792 | Accuracy(micro-avg): 0.973333 Accuracy(macro-avg): 0.973333 Log-loss: 0.052580 Log-loss reduction: 0.952140 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973333 (0.0000) Accuracy(macro-avg): 0.973333 (0.0000) Log-loss: 0.052580 (0.0000) Log-loss reduction: 0.952140 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:56 PM Time elapsed(s): 0.017 Suffix of length 27 compared against sequence of length 41 Running 'MulticlassLogisticRegression' on 'iris-tree-featurized-permuted' Running as: CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt} norm=no threads- maml.exe CV tr=MulticlassLogisticRegression{l1=0.001 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt loader=Text{col=Label:U4[0-2]:0 col=Features:1-*} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=TreeFeat{lps=2 trainer=ftr{iter=3}} xf=copy{col=Features:Leaves} Making per-feature arrays Changing data from row-wise to column-wise Processed 71 instances Binning and forming Feature objects Reserved memory for tree learner: 16380 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 45 improvement criterion: Mean Improvement L1 regularization selected 44 of 45 weights. Not training a calibrator because it is not needed. Making per-feature arrays Changing data from row-wise to column-wise Processed 79 instances Binning and forming Feature objects Reserved memory for tree learner: 17472 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Beginning optimization num vars: 48 improvement criterion: Mean Improvement L1 regularization selected 48 of 48 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 21 | 0 | 0 | 1.0000 1 || 0 | 25 | 5 | 0.8333 2 || 0 | 1 | 27 | 0.9643 ||======================== Precision ||1.0000 |0.9615 |0.8438 | Accuracy(micro-avg): 0.924051 Accuracy(macro-avg): 0.932540 Log-loss: 0.201590 Log-loss reduction: 0.814633 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 29 | 0 | 0 | 1.0000 1 || 0 | 19 | 1 | 0.9500 2 || 0 | 1 | 21 | 0.9545 ||======================== Precision ||1.0000 |0.9500 |0.9545 | Accuracy(micro-avg): 0.971831 Accuracy(macro-avg): 0.968182 Log-loss: 0.101915 Log-loss reduction: 0.906125 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.947941 (0.0239) Accuracy(macro-avg): 0.950361 (0.0178) Log-loss: 0.151753 (0.0498) Log-loss reduction: 0.860379 (0.0457) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:57 PM Time elapsed(s): 0.057 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-out.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-rp.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt Output matches baseline: 'MulticlassLogisticRegression\MulticlassLogisticRegression-CV-iris-tree-featurized-permuted.txt' Test MulticlassTreeFeaturizedLRTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.GossLightGBMTest [SKIP] LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFastRankClassificationTest with memory usage 110,743,552.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombinerWithCategoricalSplits Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFastRankClassificationTest [PASS] Output: Running 'FastRank' on 'breast-cancer' Running as: TrainTest tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer.txt} eval=Binary{pr={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\prcurve-breast-cancer-prcurve.txt }} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} eval=Binary{pr={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\prcurve-breast-cancer-prcurve.txt }} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:57 PM Time elapsed(s): 0.091 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\FastRank-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastRank\FastRank-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\FastRank-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastRank\FastRank-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\FastRank-TrainTest-breast-cancer.txt Output matches baseline: 'FastRank\FastRank-TrainTest-breast-cancer.txt' maml.exe Test eval=Binary{pr={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\prcurve-breast-cancer-prcurve.txt }} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:57 PM Time elapsed(s): 0.035 Suffix of length 33 compared against sequence of length 45 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\prcurve-breast-cancer-prcurve.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\prcurve-breast-cancer-prcurve.txt Output matches baseline: 'FastRank\prcurve-breast-cancer-prcurve.txt' Running 'FastRank' on 'breast-cancer' Running as: CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-CV-breast-cancer.txt} threads- maml.exe CV tr=FastRank{nl=5 mil=5 lr=0.25 iter=20} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 8 instances with missing features during training Processed 329 instances Binning and forming Feature objects Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 8 instances with missing features during training Processed 354 instances Binning and forming Feature objects Reserved memory for tree learner: 3816 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 131 | 3 | 0.9776 negative || 10 | 218 | 0.9561 ||====================== Precision || 0.9291 | 0.9864 | OVERALL 0/1 ACCURACY: 0.964088 LOG LOSS/instance: 0.211336 Test-set entropy (prior Log-Loss/instance): 0.950799 LOG-LOSS REDUCTION (RIG): 0.777728 AUC: 0.983225 TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 98 | 9 | 0.9159 negative || 5 | 225 | 0.9783 ||====================== Precision || 0.9515 | 0.9615 | OVERALL 0/1 ACCURACY: 0.958457 LOG LOSS/instance: 0.137700 Test-set entropy (prior Log-Loss/instance): 0.901650 LOG-LOSS REDUCTION (RIG): 0.847280 AUC: 0.993681 OVERALL RESULTS --------------------------------------- AUC: 0.988453 (0.0052) Accuracy: 0.961273 (0.0028) Positive precision: 0.940267 (0.0112) Positive recall: 0.946750 (0.0309) Negative precision: 0.973982 (0.0124) Negative recall: 0.967201 (0.0111) Log-loss: 0.174518 (0.0368) Log-loss reduction: 0.812504 (0.0348) F1 Score: 0.943030 (0.0097) AUPRC: 0.962986 (0.0211) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366094 08/11/2026 13:17:57 PM Time elapsed(s): 0.072 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\FastRank-CV-breast-cancer-out.txt Output matches baseline: 'FastRank\FastRank-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\FastRank-CV-breast-cancer-rp.txt Output matches baseline: 'FastRank\FastRank-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastRank\FastRank-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastRank\FastRank-CV-breast-cancer.txt Output matches baseline: 'FastRank\FastRank-CV-breast-cancer.txt' Test BinaryClassifierFastRankClassificationTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.RegressorFastRankTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.MulticlassifierLightGBMKeyLabelU404Test [SKIP] LightGBM is 64-bit only Microsoft.ML.RunTests.TestPredictors.RegressorOgdTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombinerWithCategoricalSplits with memory usage 110,686,208.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombinerWithCategoricalSplits [PASS] Output: Test TestTreeEnsembleCombinerWithCategoricalSplits: aborted: passed Starting test: Microsoft.ML.RunTests.TestPredictors.LinearClassifierTest Finished test: Microsoft.ML.RunTests.TestPredictors.LinearClassifierTest with memory usage 115,666,944.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.LinearClassifierTest [PASS] Output: Running 'SDCA' on 'breast-cancer' Running as: TrainTest tr=SDCA{maxIterations=5 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer.txt} Starting test: Microsoft.ML.RunTests.TestPredictors.DefaultCalibratorPerceptronTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=SDCA{maxIterations=5 checkFreq=9 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 16 instances with missing features/label during training Auto-tuning parameters: L2 = 0.014641289. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using model from last iteration. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 222 | 17 | 0.9289 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9610 | 0.9624 | OVERALL 0/1 ACCURACY: 0.961933 LOG LOSS/instance: 0.294964 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.684194 AUC: 0.995458 OVERALL RESULTS --------------------------------------- AUC: 0.995458 (0.0000) Accuracy: 0.961933 (0.0000) Positive precision: 0.961039 (0.0000) Positive recall: 0.928870 (0.0000) Negative precision: 0.962389 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.294964 (0.0000) Log-loss reduction: 0.684194 (0.0000) F1 Score: 0.944681 (0.0000) AUPRC: 0.990716 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.068 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-TrainTest-breast-cancer-out.txt Output matches baseline: 'SDCA\BinarySDCA-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-TrainTest-breast-cancer-rp.txt Output matches baseline: 'SDCA\BinarySDCA-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-TrainTest-breast-cancer.txt Output matches baseline: 'SDCA\BinarySDCA-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 222 | 17 | 0.9289 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9610 | 0.9624 | OVERALL 0/1 ACCURACY: 0.961933 LOG LOSS/instance: 0.294964 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.684194 AUC: 0.995458 OVERALL RESULTS --------------------------------------- AUC: 0.995458 (0.0000) Accuracy: 0.961933 (0.0000) Positive precision: 0.961039 (0.0000) Positive recall: 0.928870 (0.0000) Negative precision: 0.962389 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.294964 (0.0000) Log-loss reduction: 0.684194 (0.0000) F1 Score: 0.944681 (0.0000) AUPRC: 0.990716 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.023 Suffix of length 34 compared against sequence of length 42 Running 'SDCA' on 'breast-cancer' Running as: CV tr=SDCA{maxIterations=5 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-CV-breast-cancer.txt} threads- maml.exe CV tr=SDCA{maxIterations=5 checkFreq=9 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 8 instances with missing features/label during training Auto-tuning parameters: L2 = 0.030395137. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using model from last iteration. Not training a calibrator because it is not needed. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 8 instances with missing features/label during training Auto-tuning parameters: L2 = 0.028248588. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using model from last iteration. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 125 | 9 | 0.9328 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9470 | 0.9595 | OVERALL 0/1 ACCURACY: 0.954802 LOG LOSS/instance: 0.401674 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.580277 AUC: 0.993284 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 94 | 11 | 0.8952 negative || 2 | 222 | 0.9911 ||====================== Precision || 0.9792 | 0.9528 | OVERALL 0/1 ACCURACY: 0.960486 LOG LOSS/instance: 0.390543 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.567722 AUC: 0.997321 OVERALL RESULTS --------------------------------------- AUC: 0.995303 (0.0020) Accuracy: 0.957644 (0.0028) Positive precision: 0.963068 (0.0161) Positive recall: 0.914037 (0.0188) Negative precision: 0.956125 (0.0033) Negative recall: 0.979627 (0.0114) Log-loss: 0.396109 (0.0056) Log-loss reduction: 0.573999 (0.0063) F1 Score: 0.937587 (0.0023) AUPRC: 0.990827 (0.0033) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.037 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-CV-breast-cancer-out.txt Output matches baseline: 'SDCA\BinarySDCA-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-CV-breast-cancer-rp.txt Output matches baseline: 'SDCA\BinarySDCA-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-CV-breast-cancer.txt Output matches baseline: 'SDCA\BinarySDCA-CV-breast-cancer.txt' Running 'SDCA' on 'breast-cancer' Running as: TrainTest tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 16 instances with missing features/label during training Using model from last iteration. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 220 | 19 | 0.9205 negative || 6 | 438 | 0.9865 ||====================== Precision || 0.9735 | 0.9584 | OVERALL 0/1 ACCURACY: 0.963397 LOG LOSS/instance: 0.135137 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.855314 AUC: 0.996042 OVERALL RESULTS --------------------------------------- AUC: 0.996042 (0.0000) Accuracy: 0.963397 (0.0000) Positive precision: 0.973451 (0.0000) Positive recall: 0.920502 (0.0000) Negative precision: 0.958425 (0.0000) Negative recall: 0.986486 (0.0000) Log-loss: 0.135137 (0.0000) Log-loss reduction: 0.855314 (0.0000) F1 Score: 0.946237 (0.0000) AUPRC: 0.991947 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.06 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-out.txt Output matches baseline: 'SDCA\BinarySDCA-L1-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-rp.txt Output matches baseline: 'SDCA\BinarySDCA-L1-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-L1-TrainTest-breast-cancer.txt Output matches baseline: 'SDCA\BinarySDCA-L1-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 220 | 19 | 0.9205 negative || 6 | 438 | 0.9865 ||====================== Precision || 0.9735 | 0.9584 | OVERALL 0/1 ACCURACY: 0.963397 LOG LOSS/instance: 0.135137 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.855314 AUC: 0.996042 OVERALL RESULTS --------------------------------------- AUC: 0.996042 (0.0000) Accuracy: 0.963397 (0.0000) Positive precision: 0.973451 (0.0000) Positive recall: 0.920502 (0.0000) Negative precision: 0.958425 (0.0000) Negative recall: 0.986486 (0.0000) Log-loss: 0.135137 (0.0000) Log-loss reduction: 0.855314 (0.0000) F1 Score: 0.946237 (0.0000) AUPRC: 0.991947 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.028 Suffix of length 34 compared against sequence of length 40 Running 'SDCA' on 'breast-cancer' Running as: CV tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-CV-breast-cancer.txt} threads- maml.exe CV tr=SDCA{l2=1e-06 l1=0.5 maxIterations=5 checkFreq=9 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 8 instances with missing features/label during training Using model from last iteration. Not training a calibrator because it is not needed. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 8 instances with missing features/label during training Using model from last iteration. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 131 | 3 | 0.9776 negative || 8 | 212 | 0.9636 ||====================== Precision || 0.9424 | 0.9860 | OVERALL 0/1 ACCURACY: 0.968927 LOG LOSS/instance: 0.142232 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.851377 AUC: 0.993860 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 96 | 9 | 0.9143 negative || 2 | 222 | 0.9911 ||====================== Precision || 0.9796 | 0.9610 | OVERALL 0/1 ACCURACY: 0.966565 LOG LOSS/instance: 0.118621 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.868703 AUC: 0.997449 OVERALL RESULTS --------------------------------------- AUC: 0.995655 (0.0018) Accuracy: 0.967746 (0.0012) Positive precision: 0.961019 (0.0186) Positive recall: 0.945949 (0.0317) Negative precision: 0.973543 (0.0125) Negative recall: 0.977354 (0.0137) Log-loss: 0.130426 (0.0118) Log-loss reduction: 0.860040 (0.0087) F1 Score: 0.952760 (0.0069) AUPRC: 0.991454 (0.0030) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.037 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-L1-CV-breast-cancer-out.txt Output matches baseline: 'SDCA\BinarySDCA-L1-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-L1-CV-breast-cancer-rp.txt Output matches baseline: 'SDCA\BinarySDCA-L1-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-L1-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-L1-CV-breast-cancer.txt Output matches baseline: 'SDCA\BinarySDCA-L1-CV-breast-cancer.txt' Running 'SDCA' on 'breast-cancer' Running as: TrainTest tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 16 instances with missing features/label during training Using model from last iteration. Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 224 | 15 | 0.9372 negative || 8 | 436 | 0.9820 ||====================== Precision || 0.9655 | 0.9667 | OVERALL 0/1 ACCURACY: 0.966325 LOG LOSS/instance: 0.118542 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.873081 AUC: 0.995816 OVERALL RESULTS --------------------------------------- AUC: 0.995816 (0.0000) Accuracy: 0.966325 (0.0000) Positive precision: 0.965517 (0.0000) Positive recall: 0.937238 (0.0000) Negative precision: 0.966741 (0.0000) Negative recall: 0.981982 (0.0000) Log-loss: 0.118542 (0.0000) Log-loss reduction: 0.873081 (0.0000) F1 Score: 0.951168 (0.0000) AUPRC: 0.991045 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.048 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-out.txt Output matches baseline: 'SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-rp.txt Output matches baseline: 'SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt Output matches baseline: 'SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 224 | 15 | 0.9372 negative || 8 | 436 | 0.9820 ||====================== Precision || 0.9655 | 0.9667 | OVERALL 0/1 ACCURACY: 0.966325 LOG LOSS/instance: 0.118542 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.873081 AUC: 0.995816 OVERALL RESULTS --------------------------------------- AUC: 0.995816 (0.0000) Accuracy: 0.966325 (0.0000) Positive precision: 0.965517 (0.0000) Positive recall: 0.937238 (0.0000) Negative precision: 0.966741 (0.0000) Negative recall: 0.981982 (0.0000) Log-loss: 0.118542 (0.0000) Log-loss reduction: 0.873081 (0.0000) F1 Score: 0.951168 (0.0000) AUPRC: 0.991045 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:57 PM Time elapsed(s): 0.029 Suffix of length 34 compared against sequence of length 40 Running 'SDCA' on 'breast-cancer' Running as: CV tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer.txt} threads- maml.exe CV tr=SDCA{l2=1e-06 loss=SmoothedHinge l1=0.5 maxIterations=5 checkFreq=9 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 8 instances with missing features/label during training Using model from last iteration. Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Using 1 thread to train. Warning: Skipped 8 instances with missing features/label during training Using model from last iteration. Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 130 | 4 | 0.9701 negative || 8 | 212 | 0.9636 ||====================== Precision || 0.9420 | 0.9815 | OVERALL 0/1 ACCURACY: 0.966102 LOG LOSS/instance: 0.129889 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.864274 AUC: 0.994539 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 38 | 67 | 0.3619 negative || 0 | 224 | 1.0000 ||====================== Precision || 1.0000 | 0.7698 | OVERALL 0/1 ACCURACY: 0.796353 LOG LOSS/instance: 0.126797 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.859653 AUC: 0.996854 OVERALL RESULTS --------------------------------------- AUC: 0.995696 (0.0012) Accuracy: 0.881227 (0.0849) Positive precision: 0.971014 (0.0290) Positive recall: 0.666027 (0.3041) Negative precision: 0.875620 (0.1059) Negative recall: 0.981818 (0.0182) Log-loss: 0.128343 (0.0015) Log-loss reduction: 0.861964 (0.0023) F1 Score: 0.743675 (0.2122) AUPRC: 0.991479 (0.0016) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.045 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer-out.txt Output matches baseline: 'SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer-rp.txt Output matches baseline: 'SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer.txt Output matches baseline: 'SDCA\BinarySDCA-SmoothedHinge-CV-breast-cancer.txt' Running 'SGD' on 'breast-cancer' Running as: TrainTest tr=SGD{maxIterations=2 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=SGD{maxIterations=2 checkFreq=9 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 instances with missing features during training Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 225 | 14 | 0.9414 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9615 | 0.9688 | OVERALL 0/1 ACCURACY: 0.966325 LOG LOSS/instance: 0.494040 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.471051 AUC: 0.995156 OVERALL RESULTS --------------------------------------- AUC: 0.995156 (0.0000) Accuracy: 0.966325 (0.0000) Positive precision: 0.961538 (0.0000) Positive recall: 0.941423 (0.0000) Negative precision: 0.968820 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.494040 (0.0000) Log-loss reduction: 0.471051 (0.0000) F1 Score: 0.951374 (0.0000) AUPRC: 0.990094 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.048 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-TrainTest-breast-cancer-out.txt Output matches baseline: 'SGD\BinarySGD-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-TrainTest-breast-cancer-rp.txt Output matches baseline: 'SGD\BinarySGD-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-TrainTest-breast-cancer.txt Output matches baseline: 'SGD\BinarySGD-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 225 | 14 | 0.9414 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9615 | 0.9688 | OVERALL 0/1 ACCURACY: 0.966325 LOG LOSS/instance: 0.494040 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.471051 AUC: 0.995156 OVERALL RESULTS --------------------------------------- AUC: 0.995156 (0.0000) Accuracy: 0.966325 (0.0000) Positive precision: 0.961538 (0.0000) Positive recall: 0.941423 (0.0000) Negative precision: 0.968820 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.494040 (0.0000) Log-loss reduction: 0.471051 (0.0000) F1 Score: 0.951374 (0.0000) AUPRC: 0.990094 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.025 Suffix of length 34 compared against sequence of length 38 Running 'SGD' on 'breast-cancer' Running as: CV tr=SGD{maxIterations=2 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-CV-breast-cancer.txt} threads- maml.exe CV tr=SGD{maxIterations=2 checkFreq=9 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 instances with missing features during training Not training a calibrator because it is not needed. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 instances with missing features during training Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 124 | 10 | 0.9254 negative || 6 | 214 | 0.9727 ||====================== Precision || 0.9538 | 0.9554 | OVERALL 0/1 ACCURACY: 0.954802 LOG LOSS/instance: 0.670855 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.299001 AUC: 0.993046 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 97 | 8 | 0.9238 negative || 2 | 222 | 0.9911 ||====================== Precision || 0.9798 | 0.9652 | OVERALL 0/1 ACCURACY: 0.969605 LOG LOSS/instance: 0.657158 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.272615 AUC: 0.997066 OVERALL RESULTS --------------------------------------- AUC: 0.995056 (0.0020) Accuracy: 0.962204 (0.0074) Positive precision: 0.966822 (0.0130) Positive recall: 0.924591 (0.0008) Negative precision: 0.960287 (0.0049) Negative recall: 0.981899 (0.0092) Log-loss: 0.664007 (0.0068) Log-loss reduction: 0.285808 (0.0132) F1 Score: 0.945187 (0.0058) AUPRC: 0.990233 (0.0032) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.031 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-CV-breast-cancer-out.txt Output matches baseline: 'SGD\BinarySGD-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-CV-breast-cancer-rp.txt Output matches baseline: 'SGD\BinarySGD-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-CV-breast-cancer.txt Output matches baseline: 'SGD\BinarySGD-CV-breast-cancer.txt' Running 'SGD' on 'breast-cancer' Running as: TrainTest tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 instances with missing features during training Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9580 | 0.9753 | OVERALL 0/1 ACCURACY: 0.969253 LOG LOSS/instance: 0.129716 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.861119 AUC: 0.995307 OVERALL RESULTS --------------------------------------- AUC: 0.995307 (0.0000) Accuracy: 0.969253 (0.0000) Positive precision: 0.957983 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975281 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.129716 (0.0000) Log-loss reduction: 0.861119 (0.0000) F1 Score: 0.955975 (0.0000) AUPRC: 0.990403 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.056 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-out.txt Output matches baseline: 'SGD\BinarySGD-Hinge-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-rp.txt Output matches baseline: 'SGD\BinarySGD-Hinge-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-Hinge-TrainTest-breast-cancer.txt Output matches baseline: 'SGD\BinarySGD-Hinge-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9580 | 0.9753 | OVERALL 0/1 ACCURACY: 0.969253 LOG LOSS/instance: 0.129716 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.861119 AUC: 0.995307 OVERALL RESULTS --------------------------------------- AUC: 0.995307 (0.0000) Accuracy: 0.969253 (0.0000) Positive precision: 0.957983 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975281 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.129716 (0.0000) Log-loss reduction: 0.861119 (0.0000) F1 Score: 0.955975 (0.0000) AUPRC: 0.990403 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.032 Suffix of length 34 compared against sequence of length 38 Running 'SGD' on 'breast-cancer' Running as: CV tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-CV-breast-cancer.txt} threads- maml.exe CV tr=SGD{loss=Hinge maxIterations=2 checkFreq=9 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 instances with missing features during training Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 instances with missing features during training Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 125 | 9 | 0.9328 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9470 | 0.9595 | OVERALL 0/1 ACCURACY: 0.954802 LOG LOSS/instance: 0.150656 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.842575 AUC: 0.993114 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 94 | 11 | 0.8952 negative || 2 | 222 | 0.9911 ||====================== Precision || 0.9792 | 0.9528 | OVERALL 0/1 ACCURACY: 0.960486 LOG LOSS/instance: 0.127422 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.858961 AUC: 0.997109 OVERALL RESULTS --------------------------------------- AUC: 0.995111 (0.0020) Accuracy: 0.957644 (0.0028) Positive precision: 0.963068 (0.0161) Positive recall: 0.914037 (0.0188) Negative precision: 0.956125 (0.0033) Negative recall: 0.979627 (0.0114) Log-loss: 0.139039 (0.0116) Log-loss reduction: 0.850768 (0.0082) F1 Score: 0.937587 (0.0023) AUPRC: 0.990374 (0.0032) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366096 08/11/2026 13:17:58 PM Time elapsed(s): 0.047 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-Hinge-CV-breast-cancer-out.txt Output matches baseline: 'SGD\BinarySGD-Hinge-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-Hinge-CV-breast-cancer-rp.txt Output matches baseline: 'SGD\BinarySGD-Hinge-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\SGD\BinarySGD-Hinge-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\SGD\BinarySGD-Hinge-CV-breast-cancer.txt Output matches baseline: 'SGD\BinarySGD-Hinge-CV-breast-cancer.txt' Test LinearClassifierTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.RankingLightGBMTest [SKIP] Need to find ranking dataset. Microsoft.ML.RunTests.TestPredictors.GamRegressionTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.OneClassSvmLibsvmWrapperDenseTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.RegressorLightGBMRMSETest [SKIP] LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.DefaultCalibratorPerceptronTest with memory usage 115,757,056.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.DefaultCalibratorPerceptronTest [PASS] Output: Running 'AveragedPerceptron' on 'breast-cancer' Starting test: Microsoft.ML.RunTests.TestPredictors.FastForestRegressionTest Running as: TrainTest tr=AveragedPerceptron data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt} cali={} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=AveragedPerceptron cali={} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter) Not training a calibrator because a valid calibrator trainer was not provided. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 5 | 0.9791 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9512 | 0.9886 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.951220 (0.0000) Positive recall: 0.979079 (0.0000) Negative precision: 0.988558 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: NaN (0.0000) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.964948 (0.0000) AUPRC: 0.992065 (0.0000) --------------------------------------- Warning: Data does not contain a probability column. Will not output the Log-loss column Physical memory usage(MB): 126 Virtual memory usage(MB): 2366097 08/11/2026 13:17:58 PM Time elapsed(s): 0.043 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.nocalibration-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 5 | 0.9791 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9512 | 0.9886 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.951220 (0.0000) Positive recall: 0.979079 (0.0000) Negative precision: 0.988558 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: NaN (0.0000) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.964948 (0.0000) AUPRC: 0.992065 (0.0000) --------------------------------------- Warning: Data does not contain a probability column. Will not output the Log-loss column Physical memory usage(MB): 126 Virtual memory usage(MB): 2366097 08/11/2026 13:17:58 PM Time elapsed(s): 0.017 Suffix of length 35 compared against sequence of length 39 Running 'AveragedPerceptron' on 'breast-cancer' Running as: CV tr=AveragedPerceptron data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration.txt} threads- cali={} maml.exe CV tr=AveragedPerceptron threads=- cali={} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) Not training a calibrator because a valid calibrator trainer was not provided. Warning: Data does not contain a probability column. Will not output the Log-loss column Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) Not training a calibrator because a valid calibrator trainer was not provided. Warning: Data does not contain a probability column. Will not output the Log-loss column Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 133 | 1 | 0.9925 negative || 9 | 211 | 0.9591 ||====================== Precision || 0.9366 | 0.9953 | OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.994403 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 100 | 5 | 0.9524 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9709 | 0.9779 | OVERALL 0/1 ACCURACY: 0.975684 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.997619 OVERALL RESULTS --------------------------------------- AUC: 0.996011 (0.0016) Accuracy: 0.973718 (0.0020) Positive precision: 0.953747 (0.0171) Positive recall: 0.972459 (0.0201) Negative precision: 0.986580 (0.0087) Negative recall: 0.972849 (0.0138) Log-loss: NaN (NaN) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.962653 (0.0011) AUPRC: 0.992269 (0.0025) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366097 08/11/2026 13:17:58 PM Time elapsed(s): 0.033 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.nocalibration.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.nocalibration.txt' Test DefaultCalibratorPerceptronTest: completed normally: passed Test DefaultCalibratorPerceptronTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.PoissonRegressorTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.EarlyStoppingTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.PcaAnomalyTest [SKIP] Test flaky. Disabling until resolved. Microsoft.ML.RunTests.TestPredictors.WeightingRankingPredictorsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.FastForestRegressionTest with memory usage 108,228,608.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierTesterThresholdingTest Microsoft.ML.RunTests.TestPredictors.FastForestRegressionTest [PASS] Output: Running 'FastForestRegression' on 'housing' Running as: TrainTest tr=FastForestRegression{nl=5 mil=5 iter=20} data=C:\h\w\A567095C\p\test\data\housing.txt seed=1 test=C:\h\w\A567095C\p\test\data\housing.txt loader=Text{col=Label:0 col=Features:~ header=+} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\housing.txt tr=FastForestRegression{nl=5 mil=5 iter=20} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=C:\h\w\A567095C\p\test\data\housing.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 506 instances Binning and forming Feature objects Changing data from row-wise to column-wise Reserved memory for tree learner: 67032 bytes Starting to train ... Not training a calibrator because it is not needed. L1(avg): 2.970635 L2(avg): 17.744904 RMS(avg): 4.212470 Loss-fn(avg): 17.744904 R Squared: 0.789801 OVERALL RESULTS --------------------------------------- L1(avg): 2.970635 (0.0000) L2(avg): 17.744904 (0.0000) RMS(avg): 4.212470 (0.0000) Loss-fn(avg): 17.744904 (0.0000) R Squared: 0.789801 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366098 08/11/2026 13:17:58 PM Time elapsed(s): 0.25 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\FastForestRegression-TrainTest-housing-out.txt Output matches baseline: 'FastForestRegression\FastForestRegression-TrainTest-housing-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\FastForestRegression-TrainTest-housing-rp.txt Output matches baseline: 'FastForestRegression\FastForestRegression-TrainTest-housing-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\FastForestRegression-TrainTest-housing.txt Output matches baseline: 'FastForestRegression\FastForestRegression-TrainTest-housing.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing.txt data=C:\h\w\A567095C\p\test\data\housing.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-TrainTest-housing-model.zip seed=1 L1(avg): 2.970635 L2(avg): 17.744904 RMS(avg): 4.212470 Loss-fn(avg): 17.744904 R Squared: 0.789801 OVERALL RESULTS --------------------------------------- L1(avg): 2.970635 (0.0000) L2(avg): 17.744904 (0.0000) RMS(avg): 4.212470 (0.0000) Loss-fn(avg): 17.744904 (0.0000) R Squared: 0.789801 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366098 08/11/2026 13:17:58 PM Time elapsed(s): 0.023 Suffix of length 19 compared against sequence of length 29 Running 'FastForestRegression' on 'housing' Running as: CV tr=FastForestRegression{nl=5 mil=5 iter=20} data=C:\h\w\A567095C\p\test\data\housing.txt seed=1 loader=Text{col=Label:0 col=Features:~ header=+} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-CV-housing.txt} threads- maml.exe CV tr=FastForestRegression{nl=5 mil=5 iter=20} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-CV-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=C:\h\w\A567095C\p\test\data\housing.txt seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 241 instances Binning and forming Feature objects Reserved memory for tree learner: 56772 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 265 instances Binning and forming Feature objects Reserved memory for tree learner: 60444 bytes Starting to train ... Not training a calibrator because it is not needed. L1(avg): 3.297530 L2(avg): 22.053650 RMS(avg): 4.696131 Loss-fn(avg): 22.053650 R Squared: 0.736495 L1(avg): 3.262365 L2(avg): 19.891088 RMS(avg): 4.459943 Loss-fn(avg): 19.891088 R Squared: 0.766574 OVERALL RESULTS --------------------------------------- L1(avg): 3.279948 (0.0176) L2(avg): 20.972369 (1.0813) RMS(avg): 4.578037 (0.1181) Loss-fn(avg): 20.972369 (1.0813) R Squared: 0.751534 (0.0150) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366098 08/11/2026 13:17:59 PM Time elapsed(s): 0.127 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-CV-housing-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\FastForestRegression-CV-housing-out.txt Output matches baseline: 'FastForestRegression\FastForestRegression-CV-housing-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-CV-housing-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\FastForestRegression-CV-housing-rp.txt Output matches baseline: 'FastForestRegression\FastForestRegression-CV-housing-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\FastForestRegression-CV-housing.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\FastForestRegression-CV-housing.txt Output matches baseline: 'FastForestRegression\FastForestRegression-CV-housing.txt' Running 'FastForestRegression' on 'housing' Running as: TrainTest tr=FastForestRegression{nl=5 mil=5 iter=20} data=C:\h\w\A567095C\p\test\data\housing.txt seed=1 test=C:\h\w\A567095C\p\test\data\housing.txt loader=Text{col=Label:0 col=Features:~ header=+} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing.txt} scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\housing.txt tr=FastForestRegression{nl=5 mil=5 iter=20} scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=C:\h\w\A567095C\p\test\data\housing.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 506 instances Binning and forming Feature objects Changing data from row-wise to column-wise Reserved memory for tree learner: 67032 bytes Starting to train ... Not training a calibrator because it is not needed. L1(avg): 2.840810 L2(avg): 17.723068 RMS(avg): 4.209877 Loss-fn(avg): 17.723068 R Squared: 0.790060 OVERALL RESULTS --------------------------------------- L1(avg): 2.840810 (0.0000) L2(avg): 17.723068 (0.0000) RMS(avg): 4.209877 (0.0000) Loss-fn(avg): 17.723068 (0.0000) R Squared: 0.790060 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366098 08/11/2026 13:17:59 PM Time elapsed(s): 0.346 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\QuantileRegressorTester-TrainTest-housing-out.txt Output matches baseline: 'FastForestRegression\QuantileRegressorTester-TrainTest-housing-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\QuantileRegressorTester-TrainTest-housing-rp.txt Output matches baseline: 'FastForestRegression\QuantileRegressorTester-TrainTest-housing-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\QuantileRegressorTester-TrainTest-housing.txt Output matches baseline: 'FastForestRegression\QuantileRegressorTester-TrainTest-housing.txt' maml.exe Test scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing.txt data=C:\h\w\A567095C\p\test\data\housing.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-TrainTest-housing-model.zip seed=1 L1(avg): 2.840810 L2(avg): 17.723068 RMS(avg): 4.209877 Loss-fn(avg): 17.723068 R Squared: 0.790060 OVERALL RESULTS --------------------------------------- L1(avg): 2.840810 (0.0000) L2(avg): 17.723068 (0.0000) RMS(avg): 4.209877 (0.0000) Loss-fn(avg): 17.723068 (0.0000) R Squared: 0.790060 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366098 08/11/2026 13:17:59 PM Time elapsed(s): 0.236 Suffix of length 19 compared against sequence of length 29 Running 'FastForestRegression' on 'housing' Running as: CV tr=FastForestRegression{nl=5 mil=5 iter=20} data=C:\h\w\A567095C\p\test\data\housing.txt seed=1 loader=Text{col=Label:0 col=Features:~ header=+} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-CV-housing.txt} threads- scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} maml.exe CV tr=FastForestRegression{nl=5 mil=5 iter=20} scorer=QuantileRegression{quantiles = 0.25,0.5,0.75} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-CV-housing.txt loader=Text{col=Label:0 col=Features:~ header=+} data=C:\h\w\A567095C\p\test\data\housing.txt seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 241 instances Binning and forming Feature objects Reserved memory for tree learner: 56772 bytes Starting to train ... Not training a calibrator because it is not needed. Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 265 instances Binning and forming Feature objects Reserved memory for tree learner: 60444 bytes Starting to train ... Not training a calibrator because it is not needed. L1(avg): 3.180943 L2(avg): 22.529859 RMS(avg): 4.746563 Loss-fn(avg): 22.529858 R Squared: 0.730805 L1(avg): 3.240456 L2(avg): 20.642272 RMS(avg): 4.543377 Loss-fn(avg): 20.642272 R Squared: 0.757759 OVERALL RESULTS --------------------------------------- L1(avg): 3.210700 (0.0298) L2(avg): 21.586065 (0.9438) RMS(avg): 4.644970 (0.1016) Loss-fn(avg): 21.586065 (0.9438) R Squared: 0.744282 (0.0135) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:17:59 PM Time elapsed(s): 0.308 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-CV-housing-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\QuantileRegressorTester-CV-housing-out.txt Output matches baseline: 'FastForestRegression\QuantileRegressorTester-CV-housing-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-CV-housing-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\QuantileRegressorTester-CV-housing-rp.txt Output matches baseline: 'FastForestRegression\QuantileRegressorTester-CV-housing-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestRegression\QuantileRegressorTester-CV-housing.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestRegression\QuantileRegressorTester-CV-housing.txt Output matches baseline: 'FastForestRegression\QuantileRegressorTester-CV-housing.txt' Test FastForestRegressionTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.WeightingClassificationLRPredictorsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierTesterThresholdingTest with memory usage 111,448,064.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.BinaryClassifierTesterThresholdingTest [PASS] Output: Running 'LogisticRegression' on 'breast-cancer' Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold.txt} norm=no eval=BinaryClassifier{threshold=0.95 useRawScore=-} Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-model.zip seed=1 Not adding a normalizer. Warning: Skipped 16 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 198 | 41 | 0.8285 negative || 3 | 441 | 0.9932 ||====================== Precision || 0.9851 | 0.9149 | OVERALL 0/1 ACCURACY: 0.935578 LOG LOSS/instance: 0.111003 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.881154 AUC: 0.996136 OVERALL RESULTS --------------------------------------- AUC: 0.996136 (0.0000) Accuracy: 0.935578 (0.0000) Positive precision: 0.985075 (0.0000) Positive recall: 0.828452 (0.0000) Negative precision: 0.914938 (0.0000) Negative recall: 0.993243 (0.0000) Log-loss: 0.111003 (0.0000) Log-loss reduction: 0.881154 (0.0000) F1 Score: 0.900000 (0.0000) AUPRC: 0.991883 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.056 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer.withThreshold.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold.txt' maml.exe Test eval=BinaryClassifier{threshold=0.95 useRawScore=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.withThreshold-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 198 | 41 | 0.8285 negative || 3 | 441 | 0.9932 ||====================== Precision || 0.9851 | 0.9149 | OVERALL 0/1 ACCURACY: 0.935578 LOG LOSS/instance: 0.111003 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.881154 AUC: 0.996136 OVERALL RESULTS --------------------------------------- AUC: 0.996136 (0.0000) Accuracy: 0.935578 (0.0000) Positive precision: 0.985075 (0.0000) Positive recall: 0.828452 (0.0000) Negative precision: 0.914938 (0.0000) Negative recall: 0.993243 (0.0000) Log-loss: 0.111003 (0.0000) Log-loss reduction: 0.881154 (0.0000) F1 Score: 0.900000 (0.0000) AUPRC: 0.991883 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.023 Suffix of length 34 compared against sequence of length 42 Running 'LogisticRegression' on 'breast-cancer' Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold.txt} norm=no threads- eval=BinaryClassifier{threshold=0.95 useRawScore=-} maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} eval=BinaryClassifier{threshold=0.95 useRawScore=-} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 118 | 16 | 0.8806 negative || 3 | 217 | 0.9864 ||====================== Precision || 0.9752 | 0.9313 | OVERALL 0/1 ACCURACY: 0.946328 LOG LOSS/instance: 0.143504 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.850048 AUC: 0.994132 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 81 | 24 | 0.7714 negative || 0 | 224 | 1.0000 ||====================== Precision || 1.0000 | 0.9032 | OVERALL 0/1 ACCURACY: 0.927052 LOG LOSS/instance: 0.111794 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.876260 AUC: 0.997236 OVERALL RESULTS --------------------------------------- AUC: 0.995684 (0.0016) Accuracy: 0.936690 (0.0096) Positive precision: 0.987603 (0.0124) Positive recall: 0.826013 (0.0546) Negative precision: 0.917278 (0.0141) Negative recall: 0.993182 (0.0068) Log-loss: 0.127649 (0.0159) Log-loss reduction: 0.863154 (0.0131) F1 Score: 0.898229 (0.0273) AUPRC: 0.991584 (0.0025) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.053 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-CV-breast-cancer.withThreshold-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-CV-breast-cancer.withThreshold-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-CV-breast-cancer.withThreshold.txt Output matches baseline: 'LogisticRegression\LogisticRegression-CV-breast-cancer.withThreshold.txt' Test BinaryClassifierTesterThresholdingTest: completed normally: passed Test BinaryClassifierTesterThresholdingTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerTest with memory usage 112,197,632.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerTest [PASS] Output: Running 'WeightedEnsembleMulticlass' on 'iris' Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiAverage tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Starting test: Microsoft.ML.RunTests.TestPredictors.RandomCalibratorPerceptronTest Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 5 learners for the batch 1 Beginning training model 1 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 1 of 5 finished in 00:00:00.0550648 Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in 00:00:00.0804819 Beginning training model 3 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 3 of 5 finished in 00:00:00.0560703 Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 10 of 15 weights. Trainer 4 of 5 finished in 00:00:00.0652284 Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 5 of 5 finished in 00:00:00.0684459 Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 3 | 47 | 0.9400 ||======================== Precision ||1.0000 |0.9375 |0.9038 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 Log-loss: 0.433342 Log-loss reduction: 0.605555 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) Log-loss: 0.433342 (0.0000) Log-loss reduction: 0.605555 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.391 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Average-TrainTest-iris-out.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Average-TrainTest-iris-rp.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Average-TrainTest-iris.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Average-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Average-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 3 | 47 | 0.9400 ||======================== Precision ||1.0000 |0.9375 |0.9038 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 Log-loss: 0.433342 Log-loss reduction: 0.605555 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) Log-loss: 0.433342 (0.0000) Log-loss reduction: 0.605555 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.033 Suffix of length 27 compared against sequence of length 61 Test EnsemblesMultiAveragerTest: completed normally: passed Test EnsemblesMultiAveragerTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.MulticlassifierLightGBMKeyLabelTest [SKIP] LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.RandomCalibratorPerceptronTest with memory usage 115,093,504.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.RandomCalibratorPerceptronTest [PASS] Output: Running 'AveragedPerceptron' on 'breast-cancer' Running as: TrainTest tr=AveragedPerceptron data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt} numcali=200 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAveragerCombinerTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=AveragedPerceptron numcali=200 dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter) Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 5 | 0.9791 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9512 | 0.9886 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.120617 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.870860 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.951220 (0.0000) Positive recall: 0.979079 (0.0000) Negative precision: 0.988558 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: 0.120617 (0.0000) Log-loss reduction: 0.870860 (0.0000) F1 Score: 0.964948 (0.0000) AUPRC: 0.992065 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.056 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.calibrateRandom-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 5 | 0.9791 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9512 | 0.9886 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.120617 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.870860 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.951220 (0.0000) Positive recall: 0.979079 (0.0000) Negative precision: 0.988558 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: 0.120617 (0.0000) Log-loss reduction: 0.870860 (0.0000) F1 Score: 0.964948 (0.0000) AUPRC: 0.992065 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.037 Suffix of length 34 compared against sequence of length 38 Running 'AveragedPerceptron' on 'breast-cancer' Running as: CV tr=AveragedPerceptron data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt} threads- numcali=200 maml.exe CV tr=AveragedPerceptron threads=- numcali=200 dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 133 | 1 | 0.9925 negative || 9 | 211 | 0.9591 ||====================== Precision || 0.9366 | 0.9953 | OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: 0.139629 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.854097 AUC: 0.994403 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 100 | 5 | 0.9524 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9709 | 0.9779 | OVERALL 0/1 ACCURACY: 0.975684 LOG LOSS/instance: 0.121001 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.866069 AUC: 0.997619 OVERALL RESULTS --------------------------------------- AUC: 0.996011 (0.0016) Accuracy: 0.973718 (0.0020) Positive precision: 0.953747 (0.0171) Positive recall: 0.972459 (0.0201) Negative precision: 0.986580 (0.0087) Negative recall: 0.972849 (0.0138) Log-loss: 0.130315 (0.0093) Log-loss reduction: 0.860083 (0.0060) F1 Score: 0.962653 (0.0011) AUPRC: 0.992269 (0.0025) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:00 PM Time elapsed(s): 0.04 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.calibrateRandom.txt' Test RandomCalibratorPerceptronTest: completed normally: passed Test RandomCalibratorPerceptronTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.BinaryClassifierFieldAwareFactorizationMachineTest [SKIP] FieldAwareFactorizationMachine doesn't currently support non x86/x64. https://github.com/dotnet/machinelearning/issues/5871 Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAveragerCombinerTest with memory usage 116,207,616.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesAveragerCombinerTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=20 oc=Average tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiClassBootstrapSelectorTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 oc=Average tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) Trainer 1 of 20 finished in 00:00:00.0034611 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 2 of 20 finished in 00:00:00.0022042 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 3 of 20 finished in 00:00:00.0015245 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 4 of 20 finished in 00:00:00.0015753 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 20 finished in 00:00:00.0015530 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 6 of 20 finished in 00:00:00.0015612 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 7 of 20 finished in 00:00:00.0015157 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 8 of 20 finished in 00:00:00.0014801 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 9 of 20 finished in 00:00:00.0015961 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 10 of 20 finished in 00:00:00.0015344 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 11 of 20 finished in 00:00:00.0014822 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 12 of 20 finished in 00:00:00.0015239 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 13 of 20 finished in 00:00:00.0014664 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) Trainer 14 of 20 finished in 00:00:00.0014612 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 15 of 20 finished in 00:00:00.0034569 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 16 of 20 finished in 00:00:00.0015635 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 17 of 20 finished in 00:00:00.0014598 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) Trainer 18 of 20 finished in 00:00:00.0014884 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) Trainer 19 of 20 finished in 00:00:00.0014982 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 20 of 20 finished in 00:00:00.0015907 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.116558 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875206 AUC: 0.995920 OVERALL RESULTS --------------------------------------- AUC: 0.995920 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.116558 (0.0000) Log-loss reduction: 0.875206 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.991714 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:01 PM Time elapsed(s): 0.132 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Average-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-Average-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Average-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-Average-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Average-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-Average-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Average-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.116558 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875206 AUC: 0.995920 OVERALL RESULTS --------------------------------------- AUC: 0.995920 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.116558 (0.0000) Log-loss reduction: 0.875206 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.991714 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:01 PM Time elapsed(s): 0.068 Suffix of length 34 compared against sequence of length 118 Test EnsemblesAveragerCombinerTest: completed normally: passed Test EnsemblesAveragerCombinerTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiClassBootstrapSelectorTest with memory usage 122,023,936.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiClassBootstrapSelectorTest [PASS] Output: Running 'WeightedEnsembleMulticlass' on 'iris' Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=20 st=BootstrapSelector{} tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationTest Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 1 of 20 finished in 00:00:00.0465844 Beginning training model 2 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 2 of 20 finished in 00:00:00.0682115 Beginning training model 3 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 3 of 20 finished in 00:00:00.0515060 Beginning training model 4 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 10 of 15 weights. Trainer 4 of 20 finished in 00:00:00.0646126 Beginning training model 5 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 5 of 20 finished in 00:00:00.0634252 Beginning training model 6 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 6 of 20 finished in 00:00:00.0557820 Beginning training model 7 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 7 of 20 finished in 00:00:00.0706036 Beginning training model 8 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 13 of 15 weights. Trainer 8 of 20 finished in 00:00:00.0702813 Beginning training model 9 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 9 of 20 finished in 00:00:00.1153431 Beginning training model 10 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 10 of 20 finished in 00:00:00.0555219 Beginning training model 11 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 11 of 20 finished in 00:00:00.0469502 Beginning training model 12 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 12 of 20 finished in 00:00:00.0169447 Beginning training model 13 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 10 of 15 weights. Trainer 13 of 20 finished in 00:00:00.0136356 Beginning training model 14 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 14 of 20 finished in 00:00:00.0178628 Beginning training model 15 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 15 of 20 finished in 00:00:00.0226466 Beginning training model 16 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 16 of 20 finished in 00:00:00.0300219 Beginning training model 17 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 10 of 15 weights. Trainer 17 of 20 finished in 00:00:00.0214188 Beginning training model 18 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 18 of 20 finished in 00:00:00.0193449 Beginning training model 19 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 19 of 20 finished in 00:00:00.0235185 Beginning training model 20 of 20 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 20 of 20 finished in 00:00:00.0250337 Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 4 | 46 | 0.9200 ||======================== Precision ||1.0000 |0.9184 |0.9020 | Accuracy(micro-avg): 0.940000 Accuracy(macro-avg): 0.940000 Log-loss: 0.435527 Log-loss reduction: 0.603567 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.940000 (0.0000) Accuracy(macro-avg): 0.940000 (0.0000) Log-loss: 0.435527 (0.0000) Log-loss reduction: 0.603567 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:02 PM Time elapsed(s): 0.978 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Bootstrap-TrainTest-iris-out.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Bootstrap-TrainTest-iris-rp.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Bootstrap-TrainTest-iris.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Bootstrap-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 45 | 5 | 0.9000 2 || 0 | 4 | 46 | 0.9200 ||======================== Precision ||1.0000 |0.9184 |0.9020 | Accuracy(micro-avg): 0.940000 Accuracy(macro-avg): 0.940000 Log-loss: 0.435527 Log-loss reduction: 0.603567 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.940000 (0.0000) Accuracy(macro-avg): 0.940000 (0.0000) Log-loss: 0.435527 (0.0000) Log-loss reduction: 0.603567 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366099 08/11/2026 13:18:02 PM Time elapsed(s): 0.042 Suffix of length 27 compared against sequence of length 151 Test EnsemblesMultiClassBootstrapSelectorTest: completed normally: passed Test EnsemblesMultiClassBootstrapSelectorTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationTest with memory usage 111,583,232.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationTest [PASS] Output: Running 'FastTreeBinaryClassification' on 'breast-cancer' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer.txt} Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.055 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.015 Suffix of length 33 compared against sequence of length 45 Running 'FastTreeBinaryClassification' on 'breast-cancer' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 tdrop=0.5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 tdrop=0.5 lr=0.25 iter=20 mb=255} cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 7 | 0.9710 negative || 28 | 430 | 0.9389 ||====================== Precision || 0.8931 | 0.9840 | OVERALL 0/1 ACCURACY: 0.949928 LOG LOSS/instance: 0.626065 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.326318 AUC: 0.987371 OVERALL RESULTS --------------------------------------- AUC: 0.987371 (0.0000) Accuracy: 0.949928 (0.0000) Positive precision: 0.893130 (0.0000) Positive recall: 0.970954 (0.0000) Negative precision: 0.983982 (0.0000) Negative recall: 0.938865 (0.0000) Log-loss: 0.626065 (0.0000) Log-loss reduction: 0.326318 (0.0000) F1 Score: 0.930417 (0.0000) AUPRC: 0.942315 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.051 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDrop-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 7 | 0.9710 negative || 28 | 430 | 0.9389 ||====================== Precision || 0.8931 | 0.9840 | OVERALL 0/1 ACCURACY: 0.949928 LOG LOSS/instance: 0.626065 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.326318 AUC: 0.987371 OVERALL RESULTS --------------------------------------- AUC: 0.987371 (0.0000) Accuracy: 0.949928 (0.0000) Positive precision: 0.893130 (0.0000) Positive recall: 0.970954 (0.0000) Negative precision: 0.983982 (0.0000) Negative recall: 0.938865 (0.0000) Log-loss: 0.626065 (0.0000) Log-loss reduction: 0.326318 (0.0000) F1 Score: 0.930417 (0.0000) AUPRC: 0.942315 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.014 Suffix of length 33 compared against sequence of length 45 Running 'FastTreeBinaryClassification' on 'breast-cancer' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 bsr+ lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 bsr+ lr=0.25 iter=20 mb=255} cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 233 | 8 | 0.9668 negative || 28 | 430 | 0.9389 ||====================== Precision || 0.8927 | 0.9817 | OVERALL 0/1 ACCURACY: 0.948498 LOG LOSS/instance: 0.837162 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.099166 AUC: 0.983973 OVERALL RESULTS --------------------------------------- AUC: 0.983973 (0.0000) Accuracy: 0.948498 (0.0000) Positive precision: 0.892720 (0.0000) Positive recall: 0.966805 (0.0000) Negative precision: 0.981735 (0.0000) Negative recall: 0.938865 (0.0000) Log-loss: 0.837162 (0.0000) Log-loss reduction: 0.099166 (0.0000) F1 Score: 0.928287 (0.0000) AUPRC: 0.939241 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.047 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeBsr-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 233 | 8 | 0.9668 negative || 28 | 430 | 0.9389 ||====================== Precision || 0.8927 | 0.9817 | OVERALL 0/1 ACCURACY: 0.948498 LOG LOSS/instance: 0.837162 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.099166 AUC: 0.983973 OVERALL RESULTS --------------------------------------- AUC: 0.983973 (0.0000) Accuracy: 0.948498 (0.0000) Positive precision: 0.892720 (0.0000) Positive recall: 0.966805 (0.0000) Negative precision: 0.981735 (0.0000) Negative recall: 0.938865 (0.0000) Log-loss: 0.837162 (0.0000) Log-loss reduction: 0.099166 (0.0000) F1 Score: 0.928287 (0.0000) AUPRC: 0.939241 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.015 Suffix of length 33 compared against sequence of length 45 Running 'FastTreeBinaryClassification' on 'breast-cancer' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise on disk Warning: 16 of 699 examples will be skipped due to missing feature values Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise on disk Warning: 16 of 699 examples will be skipped due to missing feature values Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.07 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.015 Suffix of length 33 compared against sequence of length 45 Test FastTreeBinaryClassificationTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmTest with memory usage 116,756,480.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorPerceptronTest Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLDSvmTest [PASS] Output: Running 'LdSvm' on 'breast-cancer' Running as: TrainTest tr=LdSvm{iter=1000} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LdSvm{iter=1000} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 rows with missing feature/label values Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 11 | 433 | 0.9752 ||====================== Precision || 0.9547 | 0.9841 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.111359 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.880773 AUC: 0.996127 OVERALL RESULTS --------------------------------------- AUC: 0.996127 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.954733 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984091 (0.0000) Negative recall: 0.975225 (0.0000) Log-loss: 0.111359 (0.0000) Log-loss reduction: 0.880773 (0.0000) F1 Score: 0.962656 (0.0000) AUPRC: 0.992120 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.069 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-def-TrainTest-breast-cancer-out.txt Output matches baseline: 'LdSvm\LDSVM-def-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-def-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LdSvm\LDSVM-def-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-def-TrainTest-breast-cancer.txt Output matches baseline: 'LdSvm\LDSVM-def-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 11 | 433 | 0.9752 ||====================== Precision || 0.9547 | 0.9841 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.111359 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.880773 AUC: 0.996127 OVERALL RESULTS --------------------------------------- AUC: 0.996127 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.954733 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984091 (0.0000) Negative recall: 0.975225 (0.0000) Log-loss: 0.111359 (0.0000) Log-loss reduction: 0.880773 (0.0000) F1 Score: 0.962656 (0.0000) AUPRC: 0.992120 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.021 Suffix of length 34 compared against sequence of length 38 Running 'LdSvm' on 'breast-cancer' Running as: CV tr=LdSvm{iter=1000} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-CV-breast-cancer.txt} threads- maml.exe CV tr=LdSvm{iter=1000} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 rows with missing feature/label values Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 134 | 0 | 1.0000 negative || 10 | 210 | 0.9545 ||====================== Precision || 0.9306 | 1.0000 | OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: 0.125103 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.869275 AUC: 0.994369 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 94 | 11 | 0.8952 negative || 4 | 220 | 0.9821 ||====================== Precision || 0.9592 | 0.9524 | OVERALL 0/1 ACCURACY: 0.954407 LOG LOSS/instance: 0.283104 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.686643 AUC: 0.951786 OVERALL RESULTS --------------------------------------- AUC: 0.973077 (0.0213) Accuracy: 0.963079 (0.0087) Positive precision: 0.944870 (0.0143) Positive recall: 0.947619 (0.0524) Negative precision: 0.976190 (0.0238) Negative recall: 0.968344 (0.0138) Log-loss: 0.204103 (0.0790) Log-loss reduction: 0.777959 (0.0913) F1 Score: 0.945069 (0.0190) AUPRC: 0.974864 (0.0146) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.059 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-def-CV-breast-cancer-out.txt Output matches baseline: 'LdSvm\LDSVM-def-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-def-CV-breast-cancer-rp.txt Output matches baseline: 'LdSvm\LDSVM-def-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LdSvm\LDSVM-def-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LdSvm\win-arm\LDSVM-def-CV-breast-cancer.txt Output matches baseline: 'LdSvm\LDSVM-def-CV-breast-cancer.txt' Test BinaryClassifierLDSvmTest: completed normally: passed Test BinaryClassifierLDSvmTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.PAVCalibratorPerceptronTest with memory usage 117,784,576.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNormTest Microsoft.ML.RunTests.TestPredictors.PAVCalibratorPerceptronTest [PASS] Output: Running 'AveragedPerceptron' on 'breast-cancer' Running as: TrainTest tr=AveragedPerceptron data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt} cali=PAV maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=AveragedPerceptron cali=PAV dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 160 instances with missing features during training (over 10 iterations; 16 inst/iter) Training calibrator. PAV calibrator: piecewise function approximation has 9 components. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 5 | 0.9791 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9512 | 0.9886 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.084507 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.909522 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.951220 (0.0000) Positive recall: 0.979079 (0.0000) Negative precision: 0.988558 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: 0.084507 (0.0000) Log-loss reduction: 0.909522 (0.0000) F1 Score: 0.964948 (0.0000) AUPRC: 0.992065 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.033 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.PAVcalibration-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 5 | 0.9791 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9512 | 0.9886 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.084507 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.909522 AUC: 0.996146 OVERALL RESULTS --------------------------------------- AUC: 0.996146 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.951220 (0.0000) Positive recall: 0.979079 (0.0000) Negative precision: 0.988558 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: 0.084507 (0.0000) Log-loss reduction: 0.909522 (0.0000) F1 Score: 0.964948 (0.0000) AUPRC: 0.992065 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.016 Suffix of length 34 compared against sequence of length 39 Running 'AveragedPerceptron' on 'breast-cancer' Running as: CV tr=AveragedPerceptron data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt} threads- cali=PAV maml.exe CV tr=AveragedPerceptron threads=- cali=PAV dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) Training calibrator. PAV calibrator: piecewise function approximation has 5 components. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 80 instances with missing features during training (over 10 iterations; 8 inst/iter) Training calibrator. PAV calibrator: piecewise function approximation has 6 components. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 133 | 1 | 0.9925 negative || 9 | 211 | 0.9591 ||====================== Precision || 0.9366 | 0.9953 | OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: Infinity Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): -Infinity AUC: 0.994403 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 100 | 5 | 0.9524 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9709 | 0.9779 | OVERALL 0/1 ACCURACY: 0.975684 LOG LOSS/instance: 0.227705 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.747961 AUC: 0.997619 OVERALL RESULTS --------------------------------------- AUC: 0.996011 (0.0016) Accuracy: 0.973718 (0.0020) Positive precision: 0.953747 (0.0171) Positive recall: 0.972459 (0.0201) Negative precision: 0.986580 (0.0087) Negative recall: 0.972849 (0.0138) Log-loss: Infinity (NaN) Log-loss reduction: -Infinity (NaN) F1 Score: 0.962653 (0.0011) AUPRC: 0.992269 (0.0025) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:02 PM Time elapsed(s): 0.017 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.PAVcalibration.txt' Test PAVCalibratorPerceptronTest: completed normally: passed Test PAVCalibratorPerceptronTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.RegressorOlsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNormTest with memory usage 122,138,624.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesStackingCombinerTest Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNormTest [PASS] Output: Running 'LogisticRegression' on 'breast-cancer' Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 16 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9620 | 0.9753 | OVERALL 0/1 ACCURACY: 0.970717 LOG LOSS/instance: 0.119042 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.872546 AUC: 0.996108 OVERALL RESULTS --------------------------------------- AUC: 0.996108 (0.0000) Accuracy: 0.970717 (0.0000) Positive precision: 0.962025 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975336 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.119042 (0.0000) Log-loss reduction: 0.872546 (0.0000) F1 Score: 0.957983 (0.0000) AUPRC: 0.992030 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.034 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-norm-TrainTest-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-norm-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-norm-TrainTest-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9620 | 0.9753 | OVERALL 0/1 ACCURACY: 0.970717 LOG LOSS/instance: 0.119042 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.872546 AUC: 0.996108 OVERALL RESULTS --------------------------------------- AUC: 0.996108 (0.0000) Accuracy: 0.970717 (0.0000) Positive precision: 0.962025 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975336 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.119042 (0.0000) Log-loss reduction: 0.872546 (0.0000) F1 Score: 0.957983 (0.0000) AUPRC: 0.992030 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.017 Suffix of length 34 compared against sequence of length 42 Running 'LogisticRegression' on 'breast-cancer' Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-CV-breast-cancer.txt} threads- maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 8 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 129 | 5 | 0.9627 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9485 | 0.9771 | OVERALL 0/1 ACCURACY: 0.966102 LOG LOSS/instance: 0.137058 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.856784 AUC: 0.994166 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 96 | 9 | 0.9143 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9697 | 0.9609 | OVERALL 0/1 ACCURACY: 0.963526 LOG LOSS/instance: 0.130675 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.855361 AUC: 0.997279 OVERALL RESULTS --------------------------------------- AUC: 0.995722 (0.0016) Accuracy: 0.964814 (0.0013) Positive precision: 0.959113 (0.0106) Positive recall: 0.938486 (0.0242) Negative precision: 0.968967 (0.0081) Negative recall: 0.977394 (0.0092) Log-loss: 0.133866 (0.0032) Log-loss reduction: 0.856072 (0.0007) F1 Score: 0.948366 (0.0072) AUPRC: 0.991520 (0.0025) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.022 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-norm-CV-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-norm-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-norm-CV-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-norm-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-norm-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-norm-CV-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-norm-CV-breast-cancer.txt' Test BinaryClassifierLogisticRegressionNormTest: completed normally: passed Test BinaryClassifierLogisticRegressionNormTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.WeightingRegressionPredictorsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesStackingCombinerTest with memory usage 122,695,680.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesStackingCombinerTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestPerformanceSelectorTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=5 oc=Stacking{bp=ap} tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 5 learners for the batch 1 Beginning training model 1 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 1 of 5 finished in 00:00:00.0015292 Beginning training model 2 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 2 of 5 finished in 00:00:00.0012819 Beginning training model 3 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 3 of 5 finished in 00:00:00.0007212 Beginning training model 4 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 4 of 5 finished in 00:00:00.0006860 Beginning training model 5 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 5 finished in 00:00:00.0007098 The number of instances used for stacking trainer is 213 Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. Warning: Skipped 40 instances with missing features during training (over 10 iterations; 4 inst/iter) Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.116054 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875745 AUC: 0.996023 OVERALL RESULTS --------------------------------------- AUC: 0.996023 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.116054 (0.0000) Log-loss reduction: 0.875745 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.991901 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.072 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-StackingAP-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-StackingAP-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-StackingAP-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-StackingAP-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.116054 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875745 AUC: 0.996023 OVERALL RESULTS --------------------------------------- AUC: 0.996023 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.116054 (0.0000) Log-loss reduction: 0.875745 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.991901 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.033 Suffix of length 34 compared against sequence of length 61 Test EnsemblesStackingCombinerTest: completed normally: passed Test EnsemblesStackingCombinerTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.MulticlassLRSparseTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBestPerformanceSelectorTest with memory usage 123,408,384.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesBestPerformanceSelectorTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 pt=BestPerformanceSelector tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-model.zip seed=1 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAllDataSetSelectorTest Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 1 of 20 finished in 00:00:00.0070052 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 2 of 20 finished in 00:00:00.0024140 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 3 of 20 finished in 00:00:00.0016717 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 4 of 20 finished in 00:00:00.0064390 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 20 finished in 00:00:00.0013606 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 6 of 20 finished in 00:00:00.0021081 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 7 of 20 finished in 00:00:00.0012922 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 8 of 20 finished in 00:00:00.0012565 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 9 of 20 finished in 00:00:00.0016614 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 10 of 20 finished in 00:00:00.0015483 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) Trainer 11 of 20 finished in 00:00:00.0028604 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 12 of 20 finished in 00:00:00.0013175 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 13 of 20 finished in 00:00:00.0012720 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 14 of 20 finished in 00:00:00.0013026 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) Trainer 15 of 20 finished in 00:00:00.0013913 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) Trainer 16 of 20 finished in 00:00:00.0016224 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 17 of 20 finished in 00:00:00.0014393 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 20 instances with missing features during training (over 1 iterations; 20 inst/iter) Trainer 18 of 20 finished in 00:00:00.0023870 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 19 of 20 finished in 00:00:00.0017236 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 20 of 20 finished in 00:00:00.0017326 List of models and the metrics after sorted | AUC(Sorted) || Name of Model | | 0.9969167523124358 |LinearBinaryModelParameters | 0.9966598150051388 |LinearBinaryModelParameters | 0.9966598150051388 |LinearBinaryModelParameters | 0.9966598150051388 |LinearBinaryModelParameters | 0.9965313463514902 |LinearBinaryModelParameters | 0.9964028776978417 |LinearBinaryModelParameters | 0.9964028776978417 |LinearBinaryModelParameters | 0.9962744090441932 |LinearBinaryModelParameters | 0.9962744090441932 |LinearBinaryModelParameters | 0.9961459403905447 |LinearBinaryModelParameters | 0.9961459403905447 |LinearBinaryModelParameters | 0.9961459403905447 |LinearBinaryModelParameters | 0.9958890030832477 |LinearBinaryModelParameters | 0.9957605344295992 |LinearBinaryModelParameters | 0.9956320657759506 |LinearBinaryModelParameters | 0.9955035971223022 |LinearBinaryModelParameters | 0.9952466598150052 |LinearBinaryModelParameters | 0.9947327852004111 |LinearBinaryModelParameters | 0.994218910585817 |LinearBinaryModelParameters | 0.9939619732785201 |LinearBinaryModelParameters Warning: 10 of 20 trainings failed. Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 229 | 10 | 0.9582 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9622 | 0.9775 | OVERALL 0/1 ACCURACY: 0.972182 LOG LOSS/instance: 0.117306 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.874405 AUC: 0.996042 OVERALL RESULTS --------------------------------------- AUC: 0.996042 (0.0000) Accuracy: 0.972182 (0.0000) Positive precision: 0.962185 (0.0000) Positive recall: 0.958159 (0.0000) Negative precision: 0.977528 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.117306 (0.0000) Log-loss reduction: 0.874405 (0.0000) F1 Score: 0.960168 (0.0000) AUPRC: 0.991960 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.12 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-BestPerf-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-BestPerf-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-BestPerf-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-BestPerf-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 229 | 10 | 0.9582 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9622 | 0.9775 | OVERALL 0/1 ACCURACY: 0.972182 LOG LOSS/instance: 0.117306 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.874405 AUC: 0.996042 OVERALL RESULTS --------------------------------------- AUC: 0.996042 (0.0000) Accuracy: 0.972182 (0.0000) Positive precision: 0.962185 (0.0000) Positive recall: 0.958159 (0.0000) Negative precision: 0.977528 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.117306 (0.0000) Log-loss reduction: 0.874405 (0.0000) F1 Score: 0.960168 (0.0000) AUPRC: 0.991960 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.112 Suffix of length 34 compared against sequence of length 141 Test EnsemblesBestPerformanceSelectorTest: completed normally: passed Test EnsemblesBestPerformanceSelectorTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.MulticlassCVTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.RegressorLightGBMMAETest [SKIP] LightGBM is 64-bit only Microsoft.ML.RunTests.TestPredictors.MulticlassSdcaTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.NnConfigTests [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesAllDataSetSelectorTest with memory usage 123,486,208.00 and max memory usage 132,837,376.00 Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeHighMinDocsTest Microsoft.ML.RunTests.TestPredictors.EnsemblesAllDataSetSelectorTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 st=AllInstanceSelector tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 1 of 20 finished in 00:00:00.0014153 Beginning training model 2 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 2 of 20 finished in 00:00:00.0004477 Beginning training model 3 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 3 of 20 finished in 00:00:00.0004189 Beginning training model 4 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 4 of 20 finished in 00:00:00.0004290 Beginning training model 5 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 5 of 20 finished in 00:00:00.0004027 Beginning training model 6 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 6 of 20 finished in 00:00:00.0004031 Beginning training model 7 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 7 of 20 finished in 00:00:00.0004007 Beginning training model 8 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 8 of 20 finished in 00:00:00.0004013 Beginning training model 9 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 9 of 20 finished in 00:00:00.0004357 Beginning training model 10 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 10 of 20 finished in 00:00:00.0004103 Beginning training model 11 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 11 of 20 finished in 00:00:00.0004011 Beginning training model 12 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 12 of 20 finished in 00:00:00.0003949 Beginning training model 13 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 13 of 20 finished in 00:00:00.0004201 Beginning training model 14 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 14 of 20 finished in 00:00:00.0004200 Beginning training model 15 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 15 of 20 finished in 00:00:00.0004085 Beginning training model 16 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 16 of 20 finished in 00:00:00.0004073 Beginning training model 17 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 17 of 20 finished in 00:00:00.0004081 Beginning training model 18 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 18 of 20 finished in 00:00:00.0004332 Beginning training model 19 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 19 of 20 finished in 00:00:00.0004074 Beginning training model 20 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 20 of 20 finished in 00:00:00.0004065 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 11 | 433 | 0.9752 ||====================== Precision || 0.9547 | 0.9841 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.117326 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.874384 AUC: 0.995995 OVERALL RESULTS --------------------------------------- AUC: 0.995995 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.954733 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984091 (0.0000) Negative recall: 0.975225 (0.0000) Log-loss: 0.117326 (0.0000) Log-loss reduction: 0.874384 (0.0000) F1 Score: 0.962656 (0.0000) AUPRC: 0.991908 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.117 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-All-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-All-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-All-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-All-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-All-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-All-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-All-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 11 | 433 | 0.9752 ||====================== Precision || 0.9547 | 0.9841 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.117326 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.874384 AUC: 0.995995 OVERALL RESULTS --------------------------------------- AUC: 0.995995 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.954733 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984091 (0.0000) Negative recall: 0.975225 (0.0000) Log-loss: 0.117326 (0.0000) Log-loss reduction: 0.874384 (0.0000) F1 Score: 0.962656 (0.0000) AUPRC: 0.991908 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366103 08/11/2026 13:18:03 PM Time elapsed(s): 0.044 Suffix of length 34 compared against sequence of length 98 Test EnsemblesAllDataSetSelectorTest: completed normally: passed Test EnsemblesAllDataSetSelectorTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.TestMulticlassEnsembleCombiner [SKIP] LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeHighMinDocsTest with memory usage 116,711,424.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.FastTreeHighMinDocsTest [PASS] Output: Running 'FastTreeBinaryClassification' on 'breast-cancer' Running as: TrainTest tr=FastTreeBinaryClassification{mil=10000 iter=5} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer.txt} Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryPriorTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastTreeBinaryClassification{mil=10000 iter=5} cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 468 bytes Starting to train ... Warning: 5 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 0 | 241 | 0.0000 negative || 0 | 458 | 1.0000 ||====================== Precision || 0.0000 | 0.6552 | OVERALL 0/1 ACCURACY: 0.655222 LOG LOSS/instance: 1.000000 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): -0.076058 AUC: 0.500000 OVERALL RESULTS --------------------------------------- AUC: 0.500000 (0.0000) Accuracy: 0.655222 (0.0000) Positive precision: 0.000000 (0.0000) Positive recall: 0.000000 (0.0000) Negative precision: 0.655222 (0.0000) Negative recall: 1.000000 (0.0000) Log-loss: 1.000000 (0.0000) Log-loss reduction: -0.076058 (0.0000) F1 Score: 0.000000 (0.0000) AUPRC: 0.415719 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:03 PM Time elapsed(s): 0.047 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeHighMinDocs-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 0 | 241 | 0.0000 negative || 0 | 458 | 1.0000 ||====================== Precision || 0.0000 | 0.6552 | OVERALL 0/1 ACCURACY: 0.655222 LOG LOSS/instance: 1.000000 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): -0.076058 AUC: 0.500000 OVERALL RESULTS --------------------------------------- AUC: 0.500000 (0.0000) Accuracy: 0.655222 (0.0000) Positive precision: 0.000000 (0.0000) Positive recall: 0.000000 (0.0000) Negative precision: 0.655222 (0.0000) Negative recall: 1.000000 (0.0000) Log-loss: 1.000000 (0.0000) Log-loss reduction: -0.076058 (0.0000) F1 Score: 0.000000 (0.0000) AUPRC: 0.415719 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:03 PM Time elapsed(s): 0.012 Suffix of length 33 compared against sequence of length 46 Test FastTreeHighMinDocsTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.MulticlassLRTest [SKIP] Currently flaky on non x86/x64 devices. Disabling until we figure it out. See https://github.com/dotnet/machinelearning/issues/6684 Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryPriorTest with memory usage 121,786,368.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.BinaryPriorTest [PASS] Output: Running 'PriorPredictor' on 'breast-cancer' Running as: TrainTest tr=PriorPredictor data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:~} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=PriorPredictor dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:~} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-model.zip seed=1 Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionBinNormTest Not adding a normalizer. Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 0 | 241 | 0.0000 negative || 0 | 458 | 1.0000 ||====================== Precision || 0.0000 | 0.6552 | OVERALL 0/1 ACCURACY: 0.655222 LOG LOSS/instance: 0.929318 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.500000 OVERALL RESULTS --------------------------------------- AUC: 0.500000 (0.0000) Accuracy: 0.655222 (0.0000) Positive precision: 0.000000 (0.0000) Positive recall: 0.000000 (0.0000) Negative precision: 0.655222 (0.0000) Negative recall: 1.000000 (0.0000) Log-loss: 0.929318 (0.0000) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.000000 (0.0000) AUPRC: 0.415719 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:03 PM Time elapsed(s): 0.034 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-out.txt Output matches baseline: 'PriorPredictor\BinaryPrior-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-rp.txt Output matches baseline: 'PriorPredictor\BinaryPrior-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PriorPredictor\BinaryPrior-TrainTest-breast-cancer.txt Output matches baseline: 'PriorPredictor\BinaryPrior-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 0 | 241 | 0.0000 negative || 0 | 458 | 1.0000 ||====================== Precision || 0.0000 | 0.6552 | OVERALL 0/1 ACCURACY: 0.655222 LOG LOSS/instance: 0.929318 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.500000 OVERALL RESULTS --------------------------------------- AUC: 0.500000 (0.0000) Accuracy: 0.655222 (0.0000) Positive precision: 0.000000 (0.0000) Positive recall: 0.000000 (0.0000) Negative precision: 0.655222 (0.0000) Negative recall: 1.000000 (0.0000) Log-loss: 0.929318 (0.0000) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.000000 (0.0000) AUPRC: 0.415719 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:04 PM Time elapsed(s): 0.012 Suffix of length 33 compared against sequence of length 36 Running 'PriorPredictor' on 'breast-cancer' Running as: CV tr=PriorPredictor data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 loader=Text{col=Label:BL:0 col=Features:~} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-CV-breast-cancer.txt} threads- maml.exe CV tr=PriorPredictor threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-CV-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:~} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Not training a calibrator because it is not needed. Not adding a normalizer. Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 0 | 134 | 0.0000 negative || 0 | 228 | 1.0000 ||====================== Precision || 0.0000 | 0.6298 | OVERALL 0/1 ACCURACY: 0.629834 LOG LOSS/instance: 0.959786 Test-set entropy (prior Log-Loss/instance): 0.950799 LOG-LOSS REDUCTION (RIG): -0.009452 AUC: 0.500000 TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 0 | 107 | 0.0000 negative || 0 | 230 | 1.0000 ||====================== Precision || 0.0000 | 0.6825 | OVERALL 0/1 ACCURACY: 0.682493 LOG LOSS/instance: 0.910421 Test-set entropy (prior Log-Loss/instance): 0.901650 LOG-LOSS REDUCTION (RIG): -0.009727 AUC: 0.500000 OVERALL RESULTS --------------------------------------- AUC: 0.500000 (0.0000) Accuracy: 0.656163 (0.0263) Positive precision: 0.000000 (0.0000) Positive recall: 0.000000 (0.0000) Negative precision: 0.656163 (0.0263) Negative recall: 1.000000 (0.0000) Log-loss: 0.935104 (0.0247) Log-loss reduction: -0.009590 (0.0001) F1 Score: 0.000000 (0.0000) AUPRC: 0.418968 (0.0212) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:04 PM Time elapsed(s): 0.013 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PriorPredictor\BinaryPrior-CV-breast-cancer-out.txt Output matches baseline: 'PriorPredictor\BinaryPrior-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PriorPredictor\BinaryPrior-CV-breast-cancer-rp.txt Output matches baseline: 'PriorPredictor\BinaryPrior-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PriorPredictor\BinaryPrior-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PriorPredictor\BinaryPrior-CV-breast-cancer.txt Output matches baseline: 'PriorPredictor\BinaryPrior-CV-breast-cancer.txt' Test BinaryPriorTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionBinNormTest with memory usage 124,076,032.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionBinNormTest [PASS] Output: Running 'LogisticRegression' on 'breast-cancer' Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer.txt} xf=BinNormalizer{col=Features numBins=5} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-model.zip seed=1 xf=BinNormalizer{col=Features numBins=5} Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerSDCATest Not adding a normalizer. Warning: Skipped 16 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 9 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9587 | 0.9841 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.116898 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.874842 AUC: 0.995208 OVERALL RESULTS --------------------------------------- AUC: 0.995208 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.958678 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984127 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.116898 (0.0000) Log-loss reduction: 0.874842 (0.0000) F1 Score: 0.964657 (0.0000) AUPRC: 0.990065 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:04 PM Time elapsed(s): 0.054 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-bin-norm-TrainTest-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9587 | 0.9841 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.116898 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.874842 AUC: 0.995208 OVERALL RESULTS --------------------------------------- AUC: 0.995208 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.958678 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984127 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.116898 (0.0000) Log-loss reduction: 0.874842 (0.0000) F1 Score: 0.964657 (0.0000) AUPRC: 0.990065 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:04 PM Time elapsed(s): 0.018 Suffix of length 34 compared against sequence of length 42 Running 'LogisticRegression' on 'breast-cancer' Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer.txt} xf=BinNormalizer{col=Features numBins=5} threads- maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 xf=BinNormalizer{col=Features numBins=5} Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 9 of 10 weights. Not training a calibrator because it is not needed. Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 129 | 5 | 0.9627 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9485 | 0.9771 | OVERALL 0/1 ACCURACY: 0.966102 LOG LOSS/instance: 0.145463 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.848001 AUC: 0.992232 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 99 | 6 | 0.9429 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9706 | 0.9736 | OVERALL 0/1 ACCURACY: 0.972644 LOG LOSS/instance: 0.123323 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.863498 AUC: 0.996769 OVERALL RESULTS --------------------------------------- AUC: 0.994500 (0.0023) Accuracy: 0.969373 (0.0033) Positive precision: 0.959559 (0.0110) Positive recall: 0.952772 (0.0099) Negative precision: 0.975316 (0.0017) Negative recall: 0.977394 (0.0092) Log-loss: 0.134393 (0.0111) Log-loss reduction: 0.855749 (0.0077) F1 Score: 0.956039 (0.0005) AUPRC: 0.988987 (0.0037) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366104 08/11/2026 13:18:04 PM Time elapsed(s): 0.026 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-bin-norm-CV-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-bin-norm-CV-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-bin-norm-CV-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-bin-norm-CV-breast-cancer.txt' Test BinaryClassifierLogisticRegressionBinNormTest: completed normally: passed Test BinaryClassifierLogisticRegressionBinNormTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.FastTreeRegressionTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerSDCATest with memory usage 129,871,872.00 and max memory usage 132,837,376.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiAveragerSDCATest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNonNegativeTest Running 'WeightedEnsembleMulticlass' on 'iris' Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=WeightedEnsembleMulticlass{bp=SDCAMC{nt=1} nm=5 oc=MultiAverage tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 5 learners for the batch 1 Beginning training model 1 of 5 Using 1 thread to train. Automatically choosing a check frequency of 1. Auto-tuning parameters: maxIterations = 10563. Auto-tuning parameters: L2 = 2.6670152E-05. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using best model from iteration 958. Trainer 1 of 5 finished in 00:00:00.7158107 Beginning training model 2 of 5 Using 1 thread to train. Automatically choosing a check frequency of 1. Auto-tuning parameters: maxIterations = 8928. Auto-tuning parameters: L2 = 2.6668373E-05. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using best model from iteration 874. Trainer 2 of 5 finished in 00:00:00.8321231 Beginning training model 3 of 5 Using 1 thread to train. Automatically choosing a check frequency of 1. Auto-tuning parameters: maxIterations = 9201. Auto-tuning parameters: L2 = 2.6673779E-05. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using best model from iteration 754. Trainer 3 of 5 finished in 00:00:00.5077517 Beginning training model 4 of 5 Using 1 thread to train. Automatically choosing a check frequency of 1. Auto-tuning parameters: maxIterations = 10344. Auto-tuning parameters: L2 = 2.66688E-05. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using best model from iteration 976. Trainer 4 of 5 finished in 00:00:00.6963139 Beginning training model 5 of 5 Using 1 thread to train. Automatically choosing a check frequency of 1. Auto-tuning parameters: maxIterations = 9315. Auto-tuning parameters: L2 = 2.6674597E-05. Auto-tuning parameters: L1Threshold (L1/L2) = 0. Using best model from iteration 1058. Trainer 5 of 5 finished in 00:00:00.6220775 Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 48 | 2 | 0.9600 2 || 0 | 1 | 49 | 0.9800 ||======================== Precision ||1.0000 |0.9796 |0.9608 | Accuracy(micro-avg): 0.980000 Accuracy(macro-avg): 0.980000 Log-loss: 0.061647 Log-loss reduction: 0.943887 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.980000 (0.0000) Accuracy(macro-avg): 0.980000 (0.0000) Log-loss: 0.061647 (0.0000) Log-loss reduction: 0.943887 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366117 08/11/2026 13:18:07 PM Time elapsed(s): 3.473 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-SDCA-Average-TrainTest-iris-out.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-SDCA-Average-TrainTest-iris-rp.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-SDCA-Average-TrainTest-iris.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-SDCA-Average-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 48 | 2 | 0.9600 2 || 0 | 1 | 49 | 0.9800 ||======================== Precision ||1.0000 |0.9796 |0.9608 | Accuracy(micro-avg): 0.980000 Accuracy(macro-avg): 0.980000 Log-loss: 0.061647 Log-loss reduction: 0.943887 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.980000 (0.0000) Accuracy(macro-avg): 0.980000 (0.0000) Log-loss: 0.061647 (0.0000) Log-loss reduction: 0.943887 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366117 08/11/2026 13:18:07 PM Time elapsed(s): 0.025 Suffix of length 27 compared against sequence of length 71 Test EnsemblesMultiAveragerSDCATest: completed normally: passed Test EnsemblesMultiAveragerSDCATest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.OneClassSvmLibsvmWrapperTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.RegressorLightGBMTest [SKIP] LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNonNegativeTest with memory usage 134,770,688.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomPartitionInstanceSelectorTest Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionNonNegativeTest [PASS] Output: Running 'LogisticRegression' on 'breast-cancer' Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Warning: Skipped 16 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9580 | 0.9753 | OVERALL 0/1 ACCURACY: 0.969253 LOG LOSS/instance: 0.109007 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.883291 AUC: 0.996287 OVERALL RESULTS --------------------------------------- AUC: 0.996287 (0.0000) Accuracy: 0.969253 (0.0000) Positive precision: 0.957983 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975281 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.109007 (0.0000) Log-loss reduction: 0.883291 (0.0000) F1 Score: 0.955975 (0.0000) AUPRC: 0.992293 (0.0000) --------------------------------------- Physical memory usage(MB): 126 Virtual memory usage(MB): 2366117 08/11/2026 13:18:07 PM Time elapsed(s): 0.044 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-non-negative-TrainTest-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9580 | 0.9753 | OVERALL 0/1 ACCURACY: 0.969253 LOG LOSS/instance: 0.109007 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.883291 AUC: 0.996287 OVERALL RESULTS --------------------------------------- AUC: 0.996287 (0.0000) Accuracy: 0.969253 (0.0000) Positive precision: 0.957983 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975281 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.109007 (0.0000) Log-loss reduction: 0.883291 (0.0000) F1 Score: 0.955975 (0.0000) AUPRC: 0.992293 (0.0000) --------------------------------------- Physical memory usage(MB): 129 Virtual memory usage(MB): 2366117 08/11/2026 13:18:07 PM Time elapsed(s): 0.03 Suffix of length 34 compared against sequence of length 42 Running 'LogisticRegression' on 'breast-cancer' Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer.txt} norm=no threads- maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-4 nt=1 nn=+} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 9 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 129 | 5 | 0.9627 negative || 8 | 212 | 0.9636 ||====================== Precision || 0.9416 | 0.9770 | OVERALL 0/1 ACCURACY: 0.963277 LOG LOSS/instance: 0.140964 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.852702 AUC: 0.994437 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 96 | 9 | 0.9143 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9697 | 0.9609 | OVERALL 0/1 ACCURACY: 0.963526 LOG LOSS/instance: 0.111876 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.876169 AUC: 0.997066 OVERALL RESULTS --------------------------------------- AUC: 0.995752 (0.0013) Accuracy: 0.963401 (0.0001) Positive precision: 0.955651 (0.0140) Positive recall: 0.938486 (0.0242) Negative precision: 0.968914 (0.0080) Negative recall: 0.975122 (0.0115) Log-loss: 0.126420 (0.0145) Log-loss reduction: 0.864435 (0.0117) F1 Score: 0.946603 (0.0054) AUPRC: 0.991761 (0.0020) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366117 08/11/2026 13:18:07 PM Time elapsed(s): 0.045 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-non-negative-CV-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-non-negative-CV-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-non-negative-CV-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-non-negative-CV-breast-cancer.txt' Test BinaryClassifierLogisticRegressionNonNegativeTest: completed normally: passed Test BinaryClassifierLogisticRegressionNonNegativeTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomPartitionInstanceSelectorTest with memory usage 130,760,704.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomPartitionInstanceSelectorTest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationNoOpGroupIdTest Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=5 st=RandomPartitionSelector tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 5 learners for the batch 1 Beginning training model 1 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) Trainer 1 of 5 finished in 00:00:00.0002756 Beginning training model 2 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter) Trainer 2 of 5 finished in 00:00:00.0001795 Beginning training model 3 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter) Trainer 3 of 5 finished in 00:00:00.0001637 Beginning training model 4 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 1 instances with missing features during training (over 1 iterations; 1 inst/iter) Trainer 4 of 5 finished in 00:00:00.0001574 Beginning training model 5 of 5 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 3 instances with missing features during training (over 1 iterations; 3 inst/iter) Trainer 5 of 5 finished in 00:00:00.0001519 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 235 | 4 | 0.9833 negative || 13 | 431 | 0.9707 ||====================== Precision || 0.9476 | 0.9908 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.126392 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.864677 AUC: 0.995453 OVERALL RESULTS --------------------------------------- AUC: 0.995453 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.947581 (0.0000) Positive recall: 0.983264 (0.0000) Negative precision: 0.990805 (0.0000) Negative recall: 0.970721 (0.0000) Log-loss: 0.126392 (0.0000) Log-loss reduction: 0.864677 (0.0000) F1 Score: 0.965092 (0.0000) AUPRC: 0.990701 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366117 08/11/2026 13:18:08 PM Time elapsed(s): 0.052 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-RandomPartition-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-RandomPartition-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-RandomPartition-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomPartition-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 235 | 4 | 0.9833 negative || 13 | 431 | 0.9707 ||====================== Precision || 0.9476 | 0.9908 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.126392 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.864677 AUC: 0.995453 OVERALL RESULTS --------------------------------------- AUC: 0.995453 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.947581 (0.0000) Positive recall: 0.983264 (0.0000) Negative precision: 0.990805 (0.0000) Negative recall: 0.970721 (0.0000) Log-loss: 0.126392 (0.0000) Log-loss reduction: 0.864677 (0.0000) F1 Score: 0.965092 (0.0000) AUPRC: 0.990701 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366117 08/11/2026 13:18:08 PM Time elapsed(s): 0.032 Suffix of length 34 compared against sequence of length 58 Test EnsemblesRandomPartitionInstanceSelectorTest: completed normally: passed Test EnsemblesRandomPartitionInstanceSelectorTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationNoOpGroupIdTest with memory usage 126,496,768.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationNoOpGroupIdTest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierPerceptronTest Running 'FastTreeBinaryClassification' on 'breast-cancer-group' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{col=Label:0 col=GroupId:U4[0-10]:1 col=Features:1-*} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group.txt loader=Text{col=Label:0 col=GroupId:U4[0-10]:1 col=Features:1-*} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: This is not ranking problem, Group Id 'GroupId' column will be ignored Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: This is not ranking problem, Group Id 'GroupId' column will be ignored Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 3852 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366119 08/11/2026 13:18:08 PM Time elapsed(s): 0.054 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-breast-cancer-group-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 240 | 1 | 0.9959 negative || 13 | 445 | 0.9716 ||====================== Precision || 0.9486 | 0.9978 | OVERALL 0/1 ACCURACY: 0.979971 LOG LOSS/instance: 0.092572 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.900387 AUC: 0.995370 OVERALL RESULTS --------------------------------------- AUC: 0.995370 (0.0000) Accuracy: 0.979971 (0.0000) Positive precision: 0.948617 (0.0000) Positive recall: 0.995851 (0.0000) Negative precision: 0.997758 (0.0000) Negative recall: 0.971616 (0.0000) Log-loss: 0.092572 (0.0000) Log-loss reduction: 0.900387 (0.0000) F1 Score: 0.971660 (0.0000) AUPRC: 0.970606 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366119 08/11/2026 13:18:08 PM Time elapsed(s): 0.011 Suffix of length 33 compared against sequence of length 47 Test FastTreeBinaryClassificationNoOpGroupIdTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.LightGBMClassificationTest [SKIP] LightGBM is 64-bit only Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierPerceptronTest with memory usage 129,949,696.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.TestEnsembleCombiner Microsoft.ML.RunTests.TestPredictors.BinaryClassifierPerceptronTest [PASS] Output: Running 'AveragedPerceptron' on 'breast-cancer' Running as: TrainTest tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter) Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9508 | 0.9841 | OVERALL 0/1 ACCURACY: 0.972182 LOG LOSS/instance: 0.115962 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875844 AUC: 0.995995 OVERALL RESULTS --------------------------------------- AUC: 0.995995 (0.0000) Accuracy: 0.972182 (0.0000) Positive precision: 0.950820 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984055 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: 0.115962 (0.0000) Log-loss reduction: 0.875844 (0.0000) F1 Score: 0.960663 (0.0000) AUPRC: 0.991840 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366119 08/11/2026 13:18:08 PM Time elapsed(s): 0.044 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-TrainTest-breast-cancer.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 7 | 0.9707 negative || 12 | 432 | 0.9730 ||====================== Precision || 0.9508 | 0.9841 | OVERALL 0/1 ACCURACY: 0.972182 LOG LOSS/instance: 0.115962 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875844 AUC: 0.995995 OVERALL RESULTS --------------------------------------- AUC: 0.995995 (0.0000) Accuracy: 0.972182 (0.0000) Positive precision: 0.950820 (0.0000) Positive recall: 0.970711 (0.0000) Negative precision: 0.984055 (0.0000) Negative recall: 0.972973 (0.0000) Log-loss: 0.115962 (0.0000) Log-loss reduction: 0.875844 (0.0000) F1 Score: 0.960663 (0.0000) AUPRC: 0.991840 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366119 08/11/2026 13:18:08 PM Time elapsed(s): 0.021 Suffix of length 34 compared against sequence of length 38 Running 'AveragedPerceptron' on 'breast-cancer' Running as: CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.txt} threads- maml.exe CV tr=AveragedPerceptron{lr=0.01 iter=100 lazy+} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 132 | 2 | 0.9851 negative || 8 | 212 | 0.9636 ||====================== Precision || 0.9429 | 0.9907 | OVERALL 0/1 ACCURACY: 0.971751 LOG LOSS/instance: 0.136411 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.857460 AUC: 0.994199 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 98 | 7 | 0.9333 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9703 | 0.9693 | OVERALL 0/1 ACCURACY: 0.969605 LOG LOSS/instance: 0.118826 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.868476 AUC: 0.997577 OVERALL RESULTS --------------------------------------- AUC: 0.995888 (0.0017) Accuracy: 0.970678 (0.0011) Positive precision: 0.956577 (0.0137) Positive recall: 0.959204 (0.0259) Negative precision: 0.979976 (0.0107) Negative recall: 0.975122 (0.0115) Log-loss: 0.127618 (0.0088) Log-loss reduction: 0.862968 (0.0055) F1 Score: 0.957480 (0.0060) AUPRC: 0.992003 (0.0026) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366119 08/11/2026 13:18:08 PM Time elapsed(s): 0.035 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer-out.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer-rp.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\AveragedPerceptron\win-arm\AveragedPerceptron-CV-breast-cancer.txt Output matches baseline: 'AveragedPerceptron\AveragedPerceptron-CV-breast-cancer.txt' Test BinaryClassifierPerceptronTest: completed normally: passed Test BinaryClassifierPerceptronTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.TestEnsembleCombiner with memory usage 129,212,416.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.FastForestClassificationTest Microsoft.ML.RunTests.TestPredictors.TestEnsembleCombiner [PASS] Output: Test TestEnsembleCombiner: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.FastForestClassificationTest with memory usage 129,400,832.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesDefaultTest Microsoft.ML.RunTests.TestPredictors.FastForestClassificationTest [PASS] Output: Running 'FastForestClassification' on 'breast-cancer' Running as: TrainTest tr=FastForestClassification{nl=5 mil=10 iter=10} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=FastForestClassification{nl=5 mil=10 iter=10} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Reserved memory for tree learner: 3852 bytes Starting to train ... Training calibrator. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 7 | 0.9710 negative || 16 | 442 | 0.9651 ||====================== Precision || 0.9360 | 0.9844 | OVERALL 0/1 ACCURACY: 0.967096 LOG LOSS/instance: 0.162280 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.825377 AUC: 0.987892 OVERALL RESULTS --------------------------------------- AUC: 0.987892 (0.0000) Accuracy: 0.967096 (0.0000) Positive precision: 0.936000 (0.0000) Positive recall: 0.970954 (0.0000) Negative precision: 0.984410 (0.0000) Negative recall: 0.965066 (0.0000) Log-loss: 0.162280 (0.0000) Log-loss reduction: 0.825377 (0.0000) F1 Score: 0.953157 (0.0000) AUPRC: 0.957347 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:08 PM Time elapsed(s): 0.052 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-out.txt Output matches baseline: 'FastForestClassification\FastForestClassification-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-rp.txt Output matches baseline: 'FastForestClassification\FastForestClassification-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestClassification\FastForestClassification-TrainTest-breast-cancer.txt Output matches baseline: 'FastForestClassification\FastForestClassification-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 234 | 7 | 0.9710 negative || 16 | 442 | 0.9651 ||====================== Precision || 0.9360 | 0.9844 | OVERALL 0/1 ACCURACY: 0.967096 LOG LOSS/instance: 0.162280 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.825377 AUC: 0.987892 OVERALL RESULTS --------------------------------------- AUC: 0.987892 (0.0000) Accuracy: 0.967096 (0.0000) Positive precision: 0.936000 (0.0000) Positive recall: 0.970954 (0.0000) Negative precision: 0.984410 (0.0000) Negative recall: 0.965066 (0.0000) Log-loss: 0.162280 (0.0000) Log-loss reduction: 0.825377 (0.0000) F1 Score: 0.953157 (0.0000) AUPRC: 0.957347 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:08 PM Time elapsed(s): 0.017 Suffix of length 33 compared against sequence of length 45 Running 'FastForestClassification' on 'breast-cancer' Running as: CV tr=FastForestClassification{nl=5 mil=10 iter=10} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-CV-breast-cancer.txt} threads- maml.exe CV tr=FastForestClassification{nl=5 mil=10 iter=10} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 8 instances with missing features during training Processed 329 instances Binning and forming Feature objects Reserved memory for tree learner: 3852 bytes Starting to train ... Training calibrator. Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 8 instances with missing features during training Processed 354 instances Binning and forming Feature objects Reserved memory for tree learner: 3816 bytes Starting to train ... Training calibrator. TEST POSITIVE RATIO: 0.3702 (134.0/(134.0+228.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 127 | 7 | 0.9478 negative || 13 | 215 | 0.9430 ||====================== Precision || 0.9071 | 0.9685 | OVERALL 0/1 ACCURACY: 0.944751 LOG LOSS/instance: 0.237138 Test-set entropy (prior Log-Loss/instance): 0.950799 LOG-LOSS REDUCTION (RIG): 0.750591 AUC: 0.980312 TEST POSITIVE RATIO: 0.3175 (107.0/(107.0+230.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 100 | 7 | 0.9346 negative || 7 | 223 | 0.9696 ||====================== Precision || 0.9346 | 0.9696 | OVERALL 0/1 ACCURACY: 0.958457 LOG LOSS/instance: 0.153923 Test-set entropy (prior Log-Loss/instance): 0.901650 LOG-LOSS REDUCTION (RIG): 0.829288 AUC: 0.993722 OVERALL RESULTS --------------------------------------- AUC: 0.987017 (0.0067) Accuracy: 0.951604 (0.0069) Positive precision: 0.920861 (0.0137) Positive recall: 0.941170 (0.0066) Negative precision: 0.969017 (0.0005) Negative recall: 0.956274 (0.0133) Log-loss: 0.195530 (0.0416) Log-loss reduction: 0.789939 (0.0393) F1 Score: 0.930793 (0.0038) AUPRC: 0.961717 (0.0240) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:08 PM Time elapsed(s): 0.064 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestClassification\FastForestClassification-CV-breast-cancer-out.txt Output matches baseline: 'FastForestClassification\FastForestClassification-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestClassification\FastForestClassification-CV-breast-cancer-rp.txt Output matches baseline: 'FastForestClassification\FastForestClassification-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastForestClassification\FastForestClassification-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastForestClassification\FastForestClassification-CV-breast-cancer.txt Output matches baseline: 'FastForestClassification\FastForestClassification-CV-breast-cancer.txt' Test FastForestClassificationTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesDefaultTest with memory usage 130,560,000.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiVotingCombinerTest Microsoft.ML.RunTests.TestPredictors.EnsemblesDefaultTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{nm=20 tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) Trainer 1 of 20 finished in 00:00:00.0009913 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 2 of 20 finished in 00:00:00.0020758 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 3 of 20 finished in 00:00:00.0004516 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 4 of 20 finished in 00:00:00.0004233 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 20 finished in 00:00:00.0004157 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 6 of 20 finished in 00:00:00.0004158 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 7 of 20 finished in 00:00:00.0005566 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 8 of 20 finished in 00:00:00.0004178 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 9 of 20 finished in 00:00:00.0004263 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 10 of 20 finished in 00:00:00.0004585 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 11 of 20 finished in 00:00:00.0004381 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 12 of 20 finished in 00:00:00.0004360 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 13 of 20 finished in 00:00:00.0004284 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) Trainer 14 of 20 finished in 00:00:00.0004965 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 15 of 20 finished in 00:00:00.0004690 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 16 of 20 finished in 00:00:00.0004240 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 17 of 20 finished in 00:00:00.0004779 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) Trainer 18 of 20 finished in 00:00:00.0004530 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) Trainer 19 of 20 finished in 00:00:00.0004317 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 20 of 20 finished in 00:00:00.0004954 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.115894 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875917 AUC: 0.995976 OVERALL RESULTS --------------------------------------- AUC: 0.995976 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.115894 (0.0000) Log-loss reduction: 0.875917 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.991794 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:08 PM Time elapsed(s): 0.098 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Default-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-Default-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Default-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-Default-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Default-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-Default-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.115894 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.875917 AUC: 0.995976 OVERALL RESULTS --------------------------------------- AUC: 0.995976 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.115894 (0.0000) Log-loss reduction: 0.875917 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.991794 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:08 PM Time elapsed(s): 0.043 Suffix of length 34 compared against sequence of length 118 Running 'WeightedEnsemble' on 'breast-cancer' Running as: CV tr=WeightedEnsemble{nm=20 tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-CV-breast-cancer.txt} threads- loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe CV tr=WeightedEnsemble{nm=20 tp=-} threads=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-CV-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) Trainer 1 of 20 finished in 00:00:00.0024942 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) Trainer 2 of 20 finished in 00:00:00.0002882 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 3 of 20 finished in 00:00:00.0002544 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) Trainer 4 of 20 finished in 00:00:00.0002565 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 5 of 20 finished in 00:00:00.0002472 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) Trainer 6 of 20 finished in 00:00:00.0002441 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) Trainer 7 of 20 finished in 00:00:00.0002274 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 8 of 20 finished in 00:00:00.0002343 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) Trainer 9 of 20 finished in 00:00:00.0002315 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 10 of 20 finished in 00:00:00.0003441 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 11 of 20 finished in 00:00:00.0002407 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) Trainer 12 of 20 finished in 00:00:00.0002351 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 13 of 20 finished in 00:00:00.0002232 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 14 of 20 finished in 00:00:00.0002215 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) Trainer 15 of 20 finished in 00:00:00.0002245 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 16 of 20 finished in 00:00:00.0002195 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) Trainer 17 of 20 finished in 00:00:00.0002182 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) Trainer 18 of 20 finished in 00:00:00.0004349 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 19 of 20 finished in 00:00:00.0002569 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 20 of 20 finished in 00:00:00.0002342 Training calibrator. Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 1 of 20 finished in 00:00:00.0010847 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 2 of 20 finished in 00:00:00.0007827 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 3 of 20 finished in 00:00:00.0003164 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 4 of 20 finished in 00:00:00.0002833 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) Trainer 5 of 20 finished in 00:00:00.0002925 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 6 of 20 finished in 00:00:00.0002761 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 7 of 20 finished in 00:00:00.0002872 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 8 of 20 finished in 00:00:00.0002884 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 9 of 20 finished in 00:00:00.0003046 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 4 instances with missing features during training (over 1 iterations; 4 inst/iter) Trainer 10 of 20 finished in 00:00:00.0002800 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) Trainer 11 of 20 finished in 00:00:00.0013627 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 6 instances with missing features during training (over 1 iterations; 6 inst/iter) Trainer 12 of 20 finished in 00:00:00.0003396 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 13 of 20 finished in 00:00:00.0004554 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 14 of 20 finished in 00:00:00.0002689 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 9 instances with missing features during training (over 1 iterations; 9 inst/iter) Trainer 15 of 20 finished in 00:00:00.0002365 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 16 of 20 finished in 00:00:00.0002333 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 7 instances with missing features during training (over 1 iterations; 7 inst/iter) Trainer 17 of 20 finished in 00:00:00.0002312 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 5 instances with missing features during training (over 1 iterations; 5 inst/iter) Trainer 18 of 20 finished in 00:00:00.0002309 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 8 instances with missing features during training (over 1 iterations; 8 inst/iter) Trainer 19 of 20 finished in 00:00:00.0002449 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 10 instances with missing features during training (over 1 iterations; 10 inst/iter) Trainer 20 of 20 finished in 00:00:00.0002210 Training calibrator. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 129 | 5 | 0.9627 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9485 | 0.9771 | OVERALL 0/1 ACCURACY: 0.966102 LOG LOSS/instance: 0.143167 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.850400 AUC: 0.993996 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 103 | 2 | 0.9810 negative || 4 | 220 | 0.9821 ||====================== Precision || 0.9626 | 0.9910 | OVERALL 0/1 ACCURACY: 0.981763 LOG LOSS/instance: 0.115437 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.872227 AUC: 0.997832 OVERALL RESULTS --------------------------------------- AUC: 0.995914 (0.0019) Accuracy: 0.973932 (0.0078) Positive precision: 0.955573 (0.0070) Positive recall: 0.971819 (0.0091) Negative precision: 0.984028 (0.0070) Negative recall: 0.975162 (0.0070) Log-loss: 0.129302 (0.0139) Log-loss reduction: 0.861313 (0.0109) F1 Score: 0.963627 (0.0081) AUPRC: 0.992159 (0.0031) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:08 PM Time elapsed(s): 0.056 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Default-CV-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-Default-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Default-CV-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-Default-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Default-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Default-CV-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-Default-CV-breast-cancer.txt' Test EnsemblesDefaultTest: completed normally: passed Test EnsemblesDefaultTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.FastTreeRegressionCategoricalSplitTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiVotingCombinerTest with memory usage 131,432,448.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiVotingCombinerTest [PASS] Output: Running 'WeightedEnsembleMulticlass' on 'iris' Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris.txt} Starting test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationCategoricalSplitTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiVoting tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 5 learners for the batch 1 Beginning training model 1 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 1 of 5 finished in 00:00:00.0118509 Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in 00:00:00.0092010 Beginning training model 3 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 3 of 5 finished in 00:00:00.0074102 Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 10 of 15 weights. Trainer 4 of 5 finished in 00:00:00.0094937 Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 5 of 5 finished in 00:00:00.0105875 Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 44 | 6 | 0.8800 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9565 |0.8889 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 Log-loss: 0.511576 Log-loss reduction: 0.534344 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) Log-loss: 0.511576 (0.0000) Log-loss reduction: 0.534344 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:09 PM Time elapsed(s): 0.109 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Voting-TrainTest-iris-out.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Voting-TrainTest-iris-rp.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Voting-TrainTest-iris.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Voting-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 44 | 6 | 0.8800 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9565 |0.8889 | Accuracy(micro-avg): 0.946667 Accuracy(macro-avg): 0.946667 Log-loss: 0.511576 Log-loss reduction: 0.534344 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.946667 (0.0000) Accuracy(macro-avg): 0.946667 (0.0000) Log-loss: 0.511576 (0.0000) Log-loss reduction: 0.534344 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366126 08/11/2026 13:18:09 PM Time elapsed(s): 0.021 Suffix of length 27 compared against sequence of length 61 Test EnsemblesMultiVotingCombinerTest: completed normally: passed Test EnsemblesMultiVotingCombinerTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.WeightingClassificationFastRankPredictorsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationCategoricalSplitTest with memory usage 127,918,080.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.NoCalibratorLinearSvmTest Microsoft.ML.RunTests.TestPredictors.FastTreeBinaryClassificationCategoricalSplitTest [PASS] Output: Running 'FastTreeBinaryClassification' on 'Census-Cat-Only' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise Reserved memory for tree learner: 3180 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 55 | 60 | 0.4783 negative || 17 | 368 | 0.9558 ||====================== Precision || 0.7639 | 0.8598 | OVERALL 0/1 ACCURACY: 0.846000 LOG LOSS/instance: 0.481805 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.380722 AUC: 0.893281 OVERALL RESULTS --------------------------------------- AUC: 0.893281 (0.0000) Accuracy: 0.846000 (0.0000) Positive precision: 0.763889 (0.0000) Positive recall: 0.478261 (0.0000) Negative precision: 0.859813 (0.0000) Negative recall: 0.955844 (0.0000) Log-loss: 0.481805 (0.0000) Log-loss reduction: 0.380722 (0.0000) F1 Score: 0.588235 (0.0000) AUPRC: 0.738040 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.099 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 55 | 60 | 0.4783 negative || 17 | 368 | 0.9558 ||====================== Precision || 0.7639 | 0.8598 | OVERALL 0/1 ACCURACY: 0.846000 LOG LOSS/instance: 0.481805 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.380722 AUC: 0.893281 OVERALL RESULTS --------------------------------------- AUC: 0.893281 (0.0000) Accuracy: 0.846000 (0.0000) Positive precision: 0.763889 (0.0000) Positive recall: 0.478261 (0.0000) Negative precision: 0.859813 (0.0000) Negative recall: 0.955844 (0.0000) Log-loss: 0.481805 (0.0000) Log-loss reduction: 0.380722 (0.0000) F1 Score: 0.588235 (0.0000) AUPRC: 0.738040 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.022 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise Reserved memory for tree learner: 18324 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 75 | 40 | 0.6522 negative || 6 | 379 | 0.9844 ||====================== Precision || 0.9259 | 0.9045 | OVERALL 0/1 ACCURACY: 0.908000 LOG LOSS/instance: 0.353688 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.545395 AUC: 0.958893 OVERALL RESULTS --------------------------------------- AUC: 0.958893 (0.0000) Accuracy: 0.908000 (0.0000) Positive precision: 0.925926 (0.0000) Positive recall: 0.652174 (0.0000) Negative precision: 0.904535 (0.0000) Negative recall: 0.984416 (0.0000) Log-loss: 0.353688 (0.0000) Log-loss reduction: 0.545395 (0.0000) F1 Score: 0.765306 (0.0000) AUPRC: 0.895540 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.083 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTree-TrainTest-Census.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 75 | 40 | 0.6522 negative || 6 | 379 | 0.9844 ||====================== Precision || 0.9259 | 0.9045 | OVERALL 0/1 ACCURACY: 0.908000 LOG LOSS/instance: 0.353688 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.545395 AUC: 0.958893 OVERALL RESULTS --------------------------------------- AUC: 0.958893 (0.0000) Accuracy: 0.908000 (0.0000) Positive precision: 0.925926 (0.0000) Positive recall: 0.652174 (0.0000) Negative precision: 0.904535 (0.0000) Negative recall: 0.984416 (0.0000) Log-loss: 0.353688 (0.0000) Log-loss reduction: 0.545395 (0.0000) F1 Score: 0.765306 (0.0000) AUPRC: 0.895540 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.025 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census-Cat-Only' Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise Reserved memory for tree learner: 3180 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 67 | 48 | 0.5826 negative || 18 | 367 | 0.9532 ||====================== Precision || 0.7882 | 0.8843 | OVERALL 0/1 ACCURACY: 0.868000 LOG LOSS/instance: 0.439868 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.434625 AUC: 0.916804 OVERALL RESULTS --------------------------------------- AUC: 0.916804 (0.0000) Accuracy: 0.868000 (0.0000) Positive precision: 0.788235 (0.0000) Positive recall: 0.582609 (0.0000) Negative precision: 0.884337 (0.0000) Negative recall: 0.953247 (0.0000) Log-loss: 0.439868 (0.0000) Log-loss reduction: 0.434625 (0.0000) F1 Score: 0.670000 (0.0000) AUPRC: 0.770221 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.08 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 67 | 48 | 0.5826 negative || 18 | 367 | 0.9532 ||====================== Precision || 0.7882 | 0.8843 | OVERALL 0/1 ACCURACY: 0.868000 LOG LOSS/instance: 0.439868 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.434625 AUC: 0.916804 OVERALL RESULTS --------------------------------------- AUC: 0.916804 (0.0000) Accuracy: 0.868000 (0.0000) Positive precision: 0.788235 (0.0000) Positive recall: 0.582609 (0.0000) Negative precision: 0.884337 (0.0000) Negative recall: 0.953247 (0.0000) Log-loss: 0.439868 (0.0000) Log-loss reduction: 0.434625 (0.0000) F1 Score: 0.670000 (0.0000) AUPRC: 0.770221 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.025 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census' Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise Reserved memory for tree learner: 18324 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 83 | 32 | 0.7217 negative || 11 | 374 | 0.9714 ||====================== Precision || 0.8830 | 0.9212 | OVERALL 0/1 ACCURACY: 0.914000 LOG LOSS/instance: 0.327460 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.579106 AUC: 0.965037 OVERALL RESULTS --------------------------------------- AUC: 0.965037 (0.0000) Accuracy: 0.914000 (0.0000) Positive precision: 0.882979 (0.0000) Positive recall: 0.721739 (0.0000) Negative precision: 0.921182 (0.0000) Negative recall: 0.971429 (0.0000) Log-loss: 0.327460 (0.0000) Log-loss reduction: 0.579106 (0.0000) F1 Score: 0.794258 (0.0000) AUPRC: 0.907541 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.081 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategorical-TrainTest-Census.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 83 | 32 | 0.7217 negative || 11 | 374 | 0.9714 ||====================== Precision || 0.8830 | 0.9212 | OVERALL 0/1 ACCURACY: 0.914000 LOG LOSS/instance: 0.327460 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.579106 AUC: 0.965037 OVERALL RESULTS --------------------------------------- AUC: 0.965037 (0.0000) Accuracy: 0.914000 (0.0000) Positive precision: 0.882979 (0.0000) Positive recall: 0.721739 (0.0000) Negative precision: 0.921182 (0.0000) Negative recall: 0.971429 (0.0000) Log-loss: 0.327460 (0.0000) Log-loss reduction: 0.579106 (0.0000) F1 Score: 0.794258 (0.0000) AUPRC: 0.907541 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:09 PM Time elapsed(s): 0.021 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census-Cat-Only' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise on disk Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise on disk Reserved memory for tree learner: 11232 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 55 | 60 | 0.4783 negative || 17 | 368 | 0.9558 ||====================== Precision || 0.7639 | 0.8598 | OVERALL 0/1 ACCURACY: 0.846000 LOG LOSS/instance: 0.481805 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.380722 AUC: 0.893281 OVERALL RESULTS --------------------------------------- AUC: 0.893281 (0.0000) Accuracy: 0.846000 (0.0000) Positive precision: 0.763889 (0.0000) Positive recall: 0.478261 (0.0000) Negative precision: 0.859813 (0.0000) Negative recall: 0.955844 (0.0000) Log-loss: 0.481805 (0.0000) Log-loss reduction: 0.380722 (0.0000) F1 Score: 0.588235 (0.0000) AUPRC: 0.738040 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:10 PM Time elapsed(s): 0.169 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 55 | 60 | 0.4783 negative || 17 | 368 | 0.9558 ||====================== Precision || 0.7639 | 0.8598 | OVERALL 0/1 ACCURACY: 0.846000 LOG LOSS/instance: 0.481805 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.380722 AUC: 0.893281 OVERALL RESULTS --------------------------------------- AUC: 0.893281 (0.0000) Accuracy: 0.846000 (0.0000) Positive precision: 0.763889 (0.0000) Positive recall: 0.478261 (0.0000) Negative precision: 0.859813 (0.0000) Negative recall: 0.955844 (0.0000) Log-loss: 0.481805 (0.0000) Log-loss reduction: 0.380722 (0.0000) F1 Score: 0.588235 (0.0000) AUPRC: 0.738040 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:10 PM Time elapsed(s): 0.019 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census' Running as: TrainTest tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise on disk Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise on disk Reserved memory for tree learner: 26424 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 75 | 40 | 0.6522 negative || 6 | 379 | 0.9844 ||====================== Precision || 0.9259 | 0.9045 | OVERALL 0/1 ACCURACY: 0.908000 LOG LOSS/instance: 0.353688 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.545395 AUC: 0.958893 OVERALL RESULTS --------------------------------------- AUC: 0.958893 (0.0000) Accuracy: 0.908000 (0.0000) Positive precision: 0.925926 (0.0000) Positive recall: 0.652174 (0.0000) Negative precision: 0.904535 (0.0000) Negative recall: 0.984416 (0.0000) Log-loss: 0.353688 (0.0000) Log-loss reduction: 0.545395 (0.0000) F1 Score: 0.765306 (0.0000) AUPRC: 0.895540 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:10 PM Time elapsed(s): 0.171 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeDisk-TrainTest-Census.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 75 | 40 | 0.6522 negative || 6 | 379 | 0.9844 ||====================== Precision || 0.9259 | 0.9045 | OVERALL 0/1 ACCURACY: 0.908000 LOG LOSS/instance: 0.353688 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.545395 AUC: 0.958893 OVERALL RESULTS --------------------------------------- AUC: 0.958893 (0.0000) Accuracy: 0.908000 (0.0000) Positive precision: 0.925926 (0.0000) Positive recall: 0.652174 (0.0000) Negative precision: 0.904535 (0.0000) Negative recall: 0.984416 (0.0000) Log-loss: 0.353688 (0.0000) Log-loss reduction: 0.545395 (0.0000) F1 Score: 0.765306 (0.0000) AUPRC: 0.895540 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366127 08/11/2026 13:18:10 PM Time elapsed(s): 0.024 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census-Cat-Only' Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt loader=Text{header+ col=Label:0 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise on disk Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise on disk Reserved memory for tree learner: 4152 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 68 | 47 | 0.5913 negative || 16 | 369 | 0.9584 ||====================== Precision || 0.8095 | 0.8870 | OVERALL 0/1 ACCURACY: 0.874000 LOG LOSS/instance: 0.425411 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.453207 AUC: 0.923817 OVERALL RESULTS --------------------------------------- AUC: 0.923817 (0.0000) Accuracy: 0.874000 (0.0000) Positive precision: 0.809524 (0.0000) Positive recall: 0.591304 (0.0000) Negative precision: 0.887019 (0.0000) Negative recall: 0.958442 (0.0000) Log-loss: 0.425411 (0.0000) Log-loss reduction: 0.453207 (0.0000) F1 Score: 0.683417 (0.0000) AUPRC: 0.792176 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 3.069 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census-Cat-Only.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 68 | 47 | 0.5913 negative || 16 | 369 | 0.9584 ||====================== Precision || 0.8095 | 0.8870 | OVERALL 0/1 ACCURACY: 0.874000 LOG LOSS/instance: 0.425411 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.453207 AUC: 0.923817 OVERALL RESULTS --------------------------------------- AUC: 0.923817 (0.0000) Accuracy: 0.874000 (0.0000) Positive precision: 0.809524 (0.0000) Positive recall: 0.591304 (0.0000) Negative precision: 0.887019 (0.0000) Negative recall: 0.958442 (0.0000) Log-loss: 0.425411 (0.0000) Log-loss reduction: 0.453207 (0.0000) F1 Score: 0.683417 (0.0000) AUPRC: 0.792176 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 0.021 Suffix of length 33 compared against sequence of length 43 Running 'FastTreeBinaryClassification' on 'Census' Running as: TrainTest tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt seed=1 test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt tr=FastTreeBinaryClassification{cat=+ nl=5 mil=5 lr=0.25 iter=20 mb=255 dt+} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.txt loader=Text{header+ col=Label:0 col=Num:9-14 col=Cat:TX:1-8} data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip seed=1 xf=Cat{col=Cat} xf=Concat{col=Features:Num,Cat} Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise on disk Processed 500 instances Binning and forming Feature objects Changing data from row-wise to column-wise on disk Reserved memory for tree learner: 19344 bytes Starting to train ... Not training a calibrator because it is not needed. TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 84 | 31 | 0.7304 negative || 10 | 375 | 0.9740 ||====================== Precision || 0.8936 | 0.9236 | OVERALL 0/1 ACCURACY: 0.918000 LOG LOSS/instance: 0.322363 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.585658 AUC: 0.965624 OVERALL RESULTS --------------------------------------- AUC: 0.965624 (0.0000) Accuracy: 0.918000 (0.0000) Positive precision: 0.893617 (0.0000) Positive recall: 0.730435 (0.0000) Negative precision: 0.923645 (0.0000) Negative recall: 0.974026 (0.0000) Log-loss: 0.322363 (0.0000) Log-loss reduction: 0.585658 (0.0000) F1 Score: 0.803828 (0.0000) AUPRC: 0.910384 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 0.143 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt} Saving ini file: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt} ini={C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.ini} Saving predictor summary Saving predictor as ini Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.ini and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.ini Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.ini' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.txt Output matches baseline: 'FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat.txt data=C:\h\w\A567095C\p\test\data\adult.tiny.with-schema.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\FastTreeBinaryClassification\FastTreeCategoricalDisk-TrainTest-Census.Cat-model.zip seed=1 TEST POSITIVE RATIO: 0.2300 (115.0/(115.0+385.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 84 | 31 | 0.7304 negative || 10 | 375 | 0.9740 ||====================== Precision || 0.8936 | 0.9236 | OVERALL 0/1 ACCURACY: 0.918000 LOG LOSS/instance: 0.322363 Test-set entropy (prior Log-Loss/instance): 0.778011 LOG-LOSS REDUCTION (RIG): 0.585658 AUC: 0.965624 OVERALL RESULTS --------------------------------------- AUC: 0.965624 (0.0000) Accuracy: 0.918000 (0.0000) Positive precision: 0.893617 (0.0000) Positive recall: 0.730435 (0.0000) Negative precision: 0.923645 (0.0000) Negative recall: 0.974026 (0.0000) Log-loss: 0.322363 (0.0000) Log-loss reduction: 0.585658 (0.0000) F1 Score: 0.803828 (0.0000) AUPRC: 0.910384 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 0.062 Suffix of length 33 compared against sequence of length 43 Test FastTreeBinaryClassificationCategoricalSplitTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.NoCalibratorLinearSvmTest with memory usage 130,183,168.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.NoCalibratorLinearSvmTest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBaseLearnerTest Running 'LinearSVM' on 'breast-cancer' Running as: TrainTest tr=LinearSVM{iter=100 lambda=0.03} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration.txt} cali={} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LinearSVM{iter=100 lambda=0.03} cali={} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 1600 instances with missing features during training (over 100 iterations; 16 inst/iter) Not training a calibrator because a valid calibrator trainer was not provided. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 226 | 13 | 0.9456 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9617 | 0.9710 | OVERALL 0/1 ACCURACY: 0.967789 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.995797 OVERALL RESULTS --------------------------------------- AUC: 0.995797 (0.0000) Accuracy: 0.967789 (0.0000) Positive precision: 0.961702 (0.0000) Positive recall: 0.945607 (0.0000) Negative precision: 0.970982 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: NaN (0.0000) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.953586 (0.0000) AUPRC: 0.991453 (0.0000) --------------------------------------- Warning: Data does not contain a probability column. Will not output the Log-loss column Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 0.044 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt Output matches baseline: 'LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt Output matches baseline: 'LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration.txt Output matches baseline: 'LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-TrainTest-breast-cancer.nocalibration-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 226 | 13 | 0.9456 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9617 | 0.9710 | OVERALL 0/1 ACCURACY: 0.967789 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.995797 OVERALL RESULTS --------------------------------------- AUC: 0.995797 (0.0000) Accuracy: 0.967789 (0.0000) Positive precision: 0.961702 (0.0000) Positive recall: 0.945607 (0.0000) Negative precision: 0.970982 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: NaN (0.0000) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.953586 (0.0000) AUPRC: 0.991453 (0.0000) --------------------------------------- Warning: Data does not contain a probability column. Will not output the Log-loss column Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 0.013 Suffix of length 35 compared against sequence of length 39 Running 'LinearSVM' on 'breast-cancer' Running as: CV tr=LinearSVM{iter=100 lambda=0.03} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration.txt} threads- cali={} maml.exe CV tr=LinearSVM{iter=100 lambda=0.03} threads=- cali={} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) Not training a calibrator because a valid calibrator trainer was not provided. Warning: Data does not contain a probability column. Will not output the Log-loss column Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Warning: Skipped 800 instances with missing features during training (over 100 iterations; 8 inst/iter) Not training a calibrator because a valid calibrator trainer was not provided. Warning: Data does not contain a probability column. Will not output the Log-loss column Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 128 | 6 | 0.9552 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9481 | 0.9726 | OVERALL 0/1 ACCURACY: 0.963277 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.994233 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 97 | 8 | 0.9238 negative || 2 | 222 | 0.9911 ||====================== Precision || 0.9798 | 0.9652 | OVERALL 0/1 ACCURACY: 0.969605 LOG LOSS/instance: NaN Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.000000 AUC: 0.997491 OVERALL RESULTS --------------------------------------- AUC: 0.995862 (0.0016) Accuracy: 0.966441 (0.0032) Positive precision: 0.963973 (0.0158) Positive recall: 0.939517 (0.0157) Negative precision: 0.968910 (0.0037) Negative recall: 0.979627 (0.0114) Log-loss: NaN (NaN) Log-loss reduction: 0.000000 (0.0000) F1 Score: 0.951327 (0.0003) AUPRC: 0.991949 (0.0025) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:13 PM Time elapsed(s): 0.061 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration-out.txt Output matches baseline: 'LinearSVM\LinearSVM-CV-breast-cancer.nocalibration-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration-rp.txt Output matches baseline: 'LinearSVM\LinearSVM-CV-breast-cancer.nocalibration-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LinearSVM\LinearSVM-CV-breast-cancer.nocalibration.txt Output matches baseline: 'LinearSVM\LinearSVM-CV-breast-cancer.nocalibration.txt' Test NoCalibratorLinearSvmTest: completed normally: passed Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesBaseLearnerTest with memory usage 130,699,264.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesBaseLearnerTest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionTest Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{bp=AvgPer nm=3 tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 3 learners for the batch 1 Beginning training model 1 of 3 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 260 instances with missing features during training (over 10 iterations; 26 inst/iter) Trainer 1 of 3 finished in 00:00:00.0026131 Beginning training model 2 of 3 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) Trainer 2 of 3 finished in 00:00:00.0042671 Beginning training model 3 of 3 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 110 instances with missing features during training (over 10 iterations; 11 inst/iter) Trainer 3 of 3 finished in 00:00:00.0015498 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.112168 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.879907 AUC: 0.996240 OVERALL RESULTS --------------------------------------- AUC: 0.996240 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.112168 (0.0000) Log-loss reduction: 0.879907 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.992400 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.054 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-AvgPer-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-AvgPer-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-AvgPer-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-AvgPer-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.112168 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.879907 AUC: 0.996240 OVERALL RESULTS --------------------------------------- AUC: 0.996240 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.112168 (0.0000) Log-loss reduction: 0.879907 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.992400 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.024 Suffix of length 34 compared against sequence of length 50 Test EnsemblesBaseLearnerTest: completed normally: passed Test EnsemblesBaseLearnerTest is using win-arm configuration specific baselines. Microsoft.ML.RunTests.TestPredictors.KMeansClusteringTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionTest with memory usage 130,449,408.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLogisticRegressionTest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassReductionTest Running 'LogisticRegression' on 'breast-cancer' Running as: TrainTest tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Warning: Skipped 16 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9580 | 0.9753 | OVERALL 0/1 ACCURACY: 0.969253 LOG LOSS/instance: 0.111003 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.881154 AUC: 0.996136 OVERALL RESULTS --------------------------------------- AUC: 0.996136 (0.0000) Accuracy: 0.969253 (0.0000) Positive precision: 0.957983 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975281 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.111003 (0.0000) Log-loss reduction: 0.881154 (0.0000) F1 Score: 0.955975 (0.0000) AUPRC: 0.991883 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.019 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer-out.txt' Saving summary with: SavePredictorAs in={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-model.zip} sum={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-summary.txt} Saving predictor summary Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-summary.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer-summary.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer-summary.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-TrainTest-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 228 | 11 | 0.9540 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9580 | 0.9753 | OVERALL 0/1 ACCURACY: 0.969253 LOG LOSS/instance: 0.111003 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.881154 AUC: 0.996136 OVERALL RESULTS --------------------------------------- AUC: 0.996136 (0.0000) Accuracy: 0.969253 (0.0000) Positive precision: 0.957983 (0.0000) Positive recall: 0.953975 (0.0000) Negative precision: 0.975281 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.111003 (0.0000) Log-loss reduction: 0.881154 (0.0000) F1 Score: 0.955975 (0.0000) AUPRC: 0.991883 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.011 Suffix of length 34 compared against sequence of length 42 Running 'LogisticRegression' on 'breast-cancer' Running as: CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.txt} norm=no threads- maml.exe CV tr=LogisticRegression{l1=1.0 l2=0.1 ot=1e-3 nt=1} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Not adding a normalizer. Warning: Skipped 8 instances with missing features/label/weight during training Beginning optimization num vars: 10 improvement criterion: Mean Improvement L1 regularization selected 10 of 10 weights. Not training a calibrator because it is not needed. Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3785 (134.0/(134.0+220.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 130 | 4 | 0.9701 negative || 7 | 213 | 0.9682 ||====================== Precision || 0.9489 | 0.9816 | OVERALL 0/1 ACCURACY: 0.968927 LOG LOSS/instance: 0.143504 Test-set entropy (prior Log-Loss/instance): 0.956998 LOG-LOSS REDUCTION (RIG): 0.850048 AUC: 0.994132 Warning: The predictor produced non-finite prediction values on 8 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3191 (105.0/(105.0+224.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 96 | 9 | 0.9143 negative || 3 | 221 | 0.9866 ||====================== Precision || 0.9697 | 0.9609 | OVERALL 0/1 ACCURACY: 0.963526 LOG LOSS/instance: 0.111794 Test-set entropy (prior Log-Loss/instance): 0.903454 LOG-LOSS REDUCTION (RIG): 0.876260 AUC: 0.997236 OVERALL RESULTS --------------------------------------- AUC: 0.995684 (0.0016) Accuracy: 0.966226 (0.0027) Positive precision: 0.959301 (0.0104) Positive recall: 0.942217 (0.0279) Negative precision: 0.971218 (0.0103) Negative recall: 0.977394 (0.0092) Log-loss: 0.127649 (0.0159) Log-loss reduction: 0.863154 (0.0131) F1 Score: 0.950293 (0.0091) AUPRC: 0.991584 (0.0025) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.02 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-CV-breast-cancer-out.txt Output matches baseline: 'LogisticRegression\LogisticRegression-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-CV-breast-cancer-rp.txt Output matches baseline: 'LogisticRegression\LogisticRegression-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\LogisticRegression\LogisticRegression-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\LogisticRegression\win-arm\LogisticRegression-CV-breast-cancer.txt Output matches baseline: 'LogisticRegression\LogisticRegression-CV-breast-cancer.txt' Test BinaryClassifierLogisticRegressionTest: completed normally: passed Test BinaryClassifierLogisticRegressionTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassReductionTest with memory usage 128,192,512.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.MulticlassReductionTest [PASS] Output: Running 'OVA' on 'iris' Running as: TrainTest tr=OVA{p=AvgPer{ lr=0.8 }} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris.txt} norm=no Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesHeterogeneousTest maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=OVA{p=AvgPer{ lr=0.8 }} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Not adding a normalizer. Training learner 0 Training calibrator. Training learner 1 Training calibrator. Training learner 2 Training calibrator. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 46 | 4 | 0.9200 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9583 |0.9231 | Accuracy(micro-avg): 0.960000 Accuracy(macro-avg): 0.960000 Log-loss: 0.254771 Log-loss reduction: 0.768097 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.960000 (0.0000) Accuracy(macro-avg): 0.960000 (0.0000) Log-loss: 0.254771 (0.0000) Log-loss reduction: 0.768097 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.05 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-TrainTest-iris-out.txt Output matches baseline: 'OVA\OVA-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-TrainTest-iris-rp.txt Output matches baseline: 'OVA\OVA-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-TrainTest-iris.txt Output matches baseline: 'OVA\OVA-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 46 | 4 | 0.9200 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9583 |0.9231 | Accuracy(micro-avg): 0.960000 Accuracy(macro-avg): 0.960000 Log-loss: 0.254771 Log-loss reduction: 0.768097 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.960000 (0.0000) Accuracy(macro-avg): 0.960000 (0.0000) Log-loss: 0.254771 (0.0000) Log-loss reduction: 0.768097 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.023 Suffix of length 27 compared against sequence of length 36 Running 'OVA' on 'iris' Running as: CV tr=OVA{p=AvgPer{ lr=0.8 }} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-CV-iris.txt} norm=no threads- maml.exe CV tr=OVA{p=AvgPer{ lr=0.8 }} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-CV-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} Not adding a normalizer. Training learner 0 Training calibrator. Training learner 1 Training calibrator. Training learner 2 Training calibrator. Not training a calibrator because it is not needed. Not adding a normalizer. Training learner 0 Training calibrator. Training learner 1 Training calibrator. Training learner 2 Training calibrator. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 21 | 0 | 0 | 1.0000 1 || 0 | 28 | 2 | 0.9333 2 || 0 | 0 | 28 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.9333 | Accuracy(micro-avg): 0.974684 Accuracy(macro-avg): 0.977778 Log-loss: 0.352944 Log-loss reduction: 0.675458 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 29 | 0 | 0 | 1.0000 1 || 0 | 18 | 2 | 0.9000 2 || 0 | 0 | 22 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.9167 | Accuracy(micro-avg): 0.971831 Accuracy(macro-avg): 0.966667 Log-loss: 0.273754 Log-loss reduction: 0.747843 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973257 (0.0014) Accuracy(macro-avg): 0.972222 (0.0056) Log-loss: 0.313349 (0.0396) Log-loss reduction: 0.711651 (0.0362) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:14 PM Time elapsed(s): 0.029 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-CV-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-CV-iris-out.txt Output matches baseline: 'OVA\OVA-CV-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-CV-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-CV-iris-rp.txt Output matches baseline: 'OVA\OVA-CV-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-CV-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-CV-iris.txt Output matches baseline: 'OVA\OVA-CV-iris.txt' Running 'OVA' on 'iris' Running as: TrainTest tr=OVA{p=FastForest{ }} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=OVA{p=FastForest{ }} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Not adding a normalizer. Training learner 0 Making per-feature arrays Changing data from row-wise to column-wise Processed 150 instances Binning and forming Feature objects Reserved memory for tree learner: 20436 bytes Starting to train ... Warning: 2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Training learner 1 Making per-feature arrays Changing data from row-wise to column-wise Processed 150 instances Binning and forming Feature objects Reserved memory for tree learner: 20436 bytes Starting to train ... Warning: 3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Training learner 2 Making per-feature arrays Changing data from row-wise to column-wise Processed 150 instances Binning and forming Feature objects Reserved memory for tree learner: 20436 bytes Starting to train ... Warning: 1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 48 | 2 | 0.9600 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9600 |0.9600 | Accuracy(micro-avg): 0.973333 Accuracy(macro-avg): 0.973333 Log-loss: 0.088201 Log-loss reduction: 0.919716 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973333 (0.0000) Accuracy(macro-avg): 0.973333 (0.0000) Log-loss: 0.088201 (0.0000) Log-loss reduction: 0.919716 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.981 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-FastForest-TrainTest-iris-out.txt Output matches baseline: 'OVA\OVA-FastForest-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-FastForest-TrainTest-iris-rp.txt Output matches baseline: 'OVA\OVA-FastForest-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-FastForest-TrainTest-iris.txt Output matches baseline: 'OVA\OVA-FastForest-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 48 | 2 | 0.9600 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9600 |0.9600 | Accuracy(micro-avg): 0.973333 Accuracy(macro-avg): 0.973333 Log-loss: 0.088201 Log-loss reduction: 0.919716 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973333 (0.0000) Accuracy(macro-avg): 0.973333 (0.0000) Log-loss: 0.088201 (0.0000) Log-loss reduction: 0.919716 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.023 Suffix of length 27 compared against sequence of length 57 Running 'OVA' on 'iris' Running as: CV tr=OVA{p=FastForest{ }} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-CV-iris.txt} norm=no threads- maml.exe CV tr=OVA{p=FastForest{ }} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-CV-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} Not adding a normalizer. Training learner 0 Making per-feature arrays Changing data from row-wise to column-wise Processed 71 instances Binning and forming Feature objects Reserved memory for tree learner: 16380 bytes Starting to train ... Warning: 2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Training learner 1 Making per-feature arrays Changing data from row-wise to column-wise Processed 71 instances Binning and forming Feature objects Reserved memory for tree learner: 16380 bytes Starting to train ... Warning: 3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Training learner 2 Making per-feature arrays Changing data from row-wise to column-wise Processed 71 instances Binning and forming Feature objects Reserved memory for tree learner: 16380 bytes Starting to train ... Warning: 1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Not training a calibrator because it is not needed. Not adding a normalizer. Training learner 0 Making per-feature arrays Changing data from row-wise to column-wise Processed 79 instances Binning and forming Feature objects Reserved memory for tree learner: 17472 bytes Starting to train ... Warning: 2 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Training learner 1 Making per-feature arrays Changing data from row-wise to column-wise Processed 79 instances Binning and forming Feature objects Reserved memory for tree learner: 17472 bytes Starting to train ... Warning: 3 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Training learner 2 Making per-feature arrays Changing data from row-wise to column-wise Processed 79 instances Binning and forming Feature objects Reserved memory for tree learner: 17472 bytes Starting to train ... Warning: 1 of the boosting iterations failed to grow a tree. This is commonly because the minimum documents in leaf hyperparameter was set too high for this dataset. Training calibrator. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 21 | 0 | 0 | 1.0000 1 || 0 | 25 | 5 | 0.8333 2 || 0 | 1 | 27 | 0.9643 ||======================== Precision ||1.0000 |0.9615 |0.8438 | Accuracy(micro-avg): 0.924051 Accuracy(macro-avg): 0.932540 Log-loss: 0.197783 Log-loss reduction: 0.818133 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 29 | 0 | 0 | 1.0000 1 || 0 | 19 | 1 | 0.9500 2 || 0 | 2 | 20 | 0.9091 ||======================== Precision ||1.0000 |0.9048 |0.9524 | Accuracy(micro-avg): 0.957746 Accuracy(macro-avg): 0.953030 Log-loss: 0.103360 Log-loss reduction: 0.904794 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.940899 (0.0168) Accuracy(macro-avg): 0.942785 (0.0102) Log-loss: 0.150571 (0.0472) Log-loss reduction: 0.861464 (0.0433) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.124 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-CV-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-FastForest-CV-iris-out.txt Output matches baseline: 'OVA\OVA-FastForest-CV-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-CV-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-FastForest-CV-iris-rp.txt Output matches baseline: 'OVA\OVA-FastForest-CV-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OVA\OVA-FastForest-CV-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OVA\OVA-FastForest-CV-iris.txt Output matches baseline: 'OVA\OVA-FastForest-CV-iris.txt' Running 'PKPD' on 'iris' Running as: TrainTest tr=PKPD{p=AvgPer { lr=0.8 }} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=PKPD{p=AvgPer { lr=0.8 }} norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Not adding a normalizer. Training learner (0,0) Training calibrator. Training learner (1,0) Training calibrator. Training learner (1,1) Training calibrator. Training learner (2,0) Training calibrator. Training learner (2,1) Training calibrator. Training learner (2,2) Training calibrator. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 46 | 4 | 0.9200 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9583 |0.9231 | Accuracy(micro-avg): 0.960000 Accuracy(macro-avg): 0.960000 Log-loss: 0.255665 Log-loss reduction: 0.767284 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.960000 (0.0000) Accuracy(macro-avg): 0.960000 (0.0000) Log-loss: 0.255665 (0.0000) Log-loss reduction: 0.767284 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.106 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PKPD\PKPD-TrainTest-iris-out.txt Output matches baseline: 'PKPD\PKPD-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PKPD\PKPD-TrainTest-iris-rp.txt Output matches baseline: 'PKPD\PKPD-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PKPD\PKPD-TrainTest-iris.txt Output matches baseline: 'PKPD\PKPD-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 46 | 4 | 0.9200 2 || 0 | 2 | 48 | 0.9600 ||======================== Precision ||1.0000 |0.9583 |0.9231 | Accuracy(micro-avg): 0.960000 Accuracy(macro-avg): 0.960000 Log-loss: 0.255665 Log-loss reduction: 0.767284 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.960000 (0.0000) Accuracy(macro-avg): 0.960000 (0.0000) Log-loss: 0.255665 (0.0000) Log-loss reduction: 0.767284 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.029 Suffix of length 27 compared against sequence of length 42 Running 'PKPD' on 'iris' Running as: CV tr=PKPD{p=AvgPer { lr=0.8 }} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-CV-iris.txt} norm=no threads- maml.exe CV tr=PKPD{p=AvgPer { lr=0.8 }} threads=- norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-CV-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 xf=Term{col=Label} Not adding a normalizer. Training learner (0,0) Training calibrator. Training learner (1,0) Training calibrator. Training learner (1,1) Training calibrator. Training learner (2,0) Training calibrator. Training learner (2,1) Training calibrator. Training learner (2,2) Training calibrator. Not training a calibrator because it is not needed. Not adding a normalizer. Training learner (0,0) Training calibrator. Training learner (1,0) Training calibrator. Training learner (1,1) Training calibrator. Training learner (2,0) Training calibrator. Training learner (2,1) Training calibrator. Training learner (2,2) Training calibrator. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 21 | 0 | 0 | 1.0000 1 || 0 | 28 | 2 | 0.9333 2 || 0 | 0 | 28 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.9333 | Accuracy(micro-avg): 0.974684 Accuracy(macro-avg): 0.977778 Log-loss: 0.359335 Log-loss reduction: 0.669582 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 29 | 0 | 0 | 1.0000 1 || 0 | 18 | 2 | 0.9000 2 || 0 | 0 | 22 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.9167 | Accuracy(micro-avg): 0.971831 Accuracy(macro-avg): 0.966667 Log-loss: 0.277823 Log-loss reduction: 0.744095 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.973257 (0.0014) Accuracy(macro-avg): 0.972222 (0.0056) Log-loss: 0.318579 (0.0408) Log-loss reduction: 0.706839 (0.0373) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.029 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-CV-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PKPD\PKPD-CV-iris-out.txt Output matches baseline: 'PKPD\PKPD-CV-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-CV-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PKPD\PKPD-CV-iris-rp.txt Output matches baseline: 'PKPD\PKPD-CV-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\PKPD\PKPD-CV-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\PKPD\PKPD-CV-iris.txt Output matches baseline: 'PKPD\PKPD-CV-iris.txt' Test MulticlassReductionTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.WeightingClassificationNNPredictorsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesHeterogeneousTest with memory usage 129,110,016.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomSubSpaceSelectorTest Microsoft.ML.RunTests.TestPredictors.EnsemblesHeterogeneousTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Running as: TrainTest tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{bp=svm bp=ap nm=20 tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) Trainer 1 of 20 finished in 00:00:00.0010084 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) Trainer 2 of 20 finished in 00:00:00.0068907 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 3 of 20 finished in 00:00:00.0002773 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter) Trainer 4 of 20 finished in 00:00:00.0015547 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 20 finished in 00:00:00.0002667 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 150 instances with missing features during training (over 10 iterations; 15 inst/iter) Trainer 6 of 20 finished in 00:00:00.0015434 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 7 of 20 finished in 00:00:00.0002633 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) Trainer 8 of 20 finished in 00:00:00.0015156 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 9 of 20 finished in 00:00:00.0002914 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) Trainer 10 of 20 finished in 00:00:00.0015397 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 11 of 20 finished in 00:00:00.0002876 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 130 instances with missing features during training (over 10 iterations; 13 inst/iter) Trainer 12 of 20 finished in 00:00:00.0024290 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 13 of 20 finished in 00:00:00.0003037 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 170 instances with missing features during training (over 10 iterations; 17 inst/iter) Trainer 14 of 20 finished in 00:00:00.0015353 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 15 of 20 finished in 00:00:00.0002820 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 120 instances with missing features during training (over 10 iterations; 12 inst/iter) Trainer 16 of 20 finished in 00:00:00.0019877 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 17 of 20 finished in 00:00:00.0003119 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 190 instances with missing features during training (over 10 iterations; 19 inst/iter) Trainer 18 of 20 finished in 00:00:00.0019496 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) Trainer 19 of 20 finished in 00:00:00.0002900 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 180 instances with missing features during training (over 10 iterations; 18 inst/iter) Trainer 20 of 20 finished in 00:00:00.0015554 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9625 | 0.9819 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.112863 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.879162 AUC: 0.996249 OVERALL RESULTS --------------------------------------- AUC: 0.996249 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.962500 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981941 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.112863 (0.0000) Log-loss reduction: 0.879162 (0.0000) F1 Score: 0.964509 (0.0000) AUPRC: 0.992435 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.094 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Hetero-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Hetero-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Hetero-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Hetero-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 9 | 435 | 0.9797 ||====================== Precision || 0.9625 | 0.9819 | OVERALL 0/1 ACCURACY: 0.975110 LOG LOSS/instance: 0.112863 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.879162 AUC: 0.996249 OVERALL RESULTS --------------------------------------- AUC: 0.996249 (0.0000) Accuracy: 0.975110 (0.0000) Positive precision: 0.962500 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981941 (0.0000) Negative recall: 0.979730 (0.0000) Log-loss: 0.112863 (0.0000) Log-loss reduction: 0.879162 (0.0000) F1 Score: 0.964509 (0.0000) AUPRC: 0.992435 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:15 PM Time elapsed(s): 0.039 Suffix of length 34 compared against sequence of length 118 Test EnsemblesHeterogeneousTest: completed normally: passed Test EnsemblesHeterogeneousTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomSubSpaceSelectorTest with memory usage 131,358,720.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesRandomSubSpaceSelectorTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Starting test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombiner Running as: TrainTest tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 st=AllInstanceSelector{fs=RandomFeatureSelector} tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 1 of 20 finished in 00:00:00.0026572 Beginning training model 2 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 2 of 20 finished in 00:00:00.0010339 Beginning training model 3 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 3 of 20 finished in 00:00:00.0001697 Beginning training model 4 of 20 Trainer 4 of 20 finished in 00:00:00.0005703 Beginning training model 5 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 5 of 20 finished in 00:00:00.0006171 Beginning training model 6 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 6 of 20 finished in 00:00:00.0005220 Beginning training model 7 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 7 of 20 finished in 00:00:00.0004577 Beginning training model 8 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 8 of 20 finished in 00:00:00.0009438 Beginning training model 9 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 9 of 20 finished in 00:00:00.0006365 Beginning training model 10 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 10 of 20 finished in 00:00:00.0002421 Beginning training model 11 of 20 Trainer 11 of 20 finished in 00:00:00.0006050 Beginning training model 12 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 12 of 20 finished in 00:00:00.0002060 Beginning training model 13 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 13 of 20 finished in 00:00:00.0004459 Beginning training model 14 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 14 of 20 finished in 00:00:00.0004547 Beginning training model 15 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 15 of 20 finished in 00:00:00.0034852 Beginning training model 16 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 16 of 20 finished in 00:00:00.0004994 Beginning training model 17 of 20 Trainer 17 of 20 finished in 00:00:00.0004043 Beginning training model 18 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 18 of 20 finished in 00:00:00.0004532 Beginning training model 19 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 19 of 20 finished in 00:00:00.0004327 Beginning training model 20 of 20 Warning: Skipped 16 instances with missing features during training (over 1 iterations; 16 inst/iter) Trainer 20 of 20 finished in 00:00:00.0005508 Training calibrator. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 233 | 8 | 0.9668 negative || 12 | 446 | 0.9738 ||====================== Precision || 0.9510 | 0.9824 | OVERALL 0/1 ACCURACY: 0.971388 LOG LOSS/instance: 0.130701 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.859358 AUC: 0.994931 OVERALL RESULTS --------------------------------------- AUC: 0.994931 (0.0000) Accuracy: 0.971388 (0.0000) Positive precision: 0.951020 (0.0000) Positive recall: 0.966805 (0.0000) Negative precision: 0.982379 (0.0000) Negative recall: 0.973799 (0.0000) Log-loss: 0.130701 (0.0000) Log-loss reduction: 0.859358 (0.0000) F1 Score: 0.958848 (0.0000) AUPRC: 0.989467 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.085 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-RandomFeature-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-RandomFeature-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-RandomFeature-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-RandomFeature-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 233 | 8 | 0.9668 negative || 12 | 446 | 0.9738 ||====================== Precision || 0.9510 | 0.9824 | OVERALL 0/1 ACCURACY: 0.971388 LOG LOSS/instance: 0.130701 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.859358 AUC: 0.994931 OVERALL RESULTS --------------------------------------- AUC: 0.994931 (0.0000) Accuracy: 0.971388 (0.0000) Positive precision: 0.951020 (0.0000) Positive recall: 0.966805 (0.0000) Negative precision: 0.982379 (0.0000) Negative recall: 0.973799 (0.0000) Log-loss: 0.130701 (0.0000) Log-loss reduction: 0.859358 (0.0000) F1 Score: 0.958848 (0.0000) AUPRC: 0.989467 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.043 Suffix of length 33 compared against sequence of length 94 Test EnsemblesRandomSubSpaceSelectorTest: completed normally: passed Test EnsemblesRandomSubSpaceSelectorTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombiner with memory usage 129,753,088.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.TestTreeEnsembleCombiner [PASS] Output: Test TestTreeEnsembleCombiner: aborted: passed Starting test: Microsoft.ML.RunTests.TestPredictors.MulticlassNaiveBayes Microsoft.ML.RunTests.TestPredictors.BinaryClassifierLinearSvmTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.PoissonRegressorNonNegativeTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.MulticlassNaiveBayes with memory usage 130,486,272.00 and max memory usage 136,867,840.00 Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesVotingCombinerTest Microsoft.ML.RunTests.TestPredictors.MulticlassNaiveBayes [PASS] Output: Running 'MultiClassNaiveBayes' on 'breast-cancer' Running as: TrainTest tr=MultiClassNaiveBayes data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- out={C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=MultiClassNaiveBayes cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Not training a calibrator because it is not needed. Confusion table ||====================== PREDICTED || 0 | 1 | Recall TRUTH ||====================== 0 || 458 | 0 | 1.0000 1 || 241 | 0 | 0.0000 ||====================== Precision || 0.6552 | 0.0000 | Accuracy(micro-avg): 0.655222 Accuracy(macro-avg): 0.500000 Log-loss: 34.538776 Log-loss reduction: -52.618809 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.655222 (0.0000) Accuracy(macro-avg): 0.500000 (0.0000) Log-loss: 34.538776 (0.0000) Log-loss reduction: -52.618809 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.02 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-out.txt Output matches baseline: 'MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-rp.txt Output matches baseline: 'MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer.txt Output matches baseline: 'MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-TrainTest-breast-cancer-model.zip seed=1 Confusion table ||====================== PREDICTED || 0 | 1 | Recall TRUTH ||====================== 0 || 458 | 0 | 1.0000 1 || 241 | 0 | 0.0000 ||====================== Precision || 0.6552 | 0.0000 | Accuracy(micro-avg): 0.655222 Accuracy(macro-avg): 0.500000 Log-loss: 34.538776 Log-loss reduction: -52.618809 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.655222 (0.0000) Accuracy(macro-avg): 0.500000 (0.0000) Log-loss: 34.538776 (0.0000) Log-loss reduction: -52.618809 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.011 Suffix of length 26 compared against sequence of length 29 Running 'MultiClassNaiveBayes' on 'breast-cancer' Running as: CV tr=MultiClassNaiveBayes data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} cache- dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer.txt} threads- maml.exe CV tr=MultiClassNaiveBayes threads=- cache=- dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer.txt loader=Text{sparse- col=Attr:TX:6 col=Label:0 col=Features:1-5,6,7-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 Not adding a normalizer. Not training a calibrator because it is not needed. Not adding a normalizer. Not training a calibrator because it is not needed. Confusion table ||====================== PREDICTED || 0 | 1 | Recall TRUTH ||====================== 0 || 228 | 0 | 1.0000 1 || 134 | 0 | 0.0000 ||====================== Precision || 0.6298 | 0.0000 | Accuracy(micro-avg): 0.629834 Accuracy(macro-avg): 0.500000 Log-loss: 34.538776 Log-loss reduction: -51.407404 Confusion table ||====================== PREDICTED || 0 | 1 | Recall TRUTH ||====================== 0 || 230 | 0 | 1.0000 1 || 107 | 0 | 0.0000 ||====================== Precision || 0.6825 | 0.0000 | Accuracy(micro-avg): 0.682493 Accuracy(macro-avg): 0.500000 Log-loss: 34.538776 Log-loss reduction: -54.264136 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.656163 (0.0263) Accuracy(macro-avg): 0.500000 (0.0000) Log-loss: 34.538776 (0.0000) Log-loss reduction: -52.835770 (1.4284) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.021 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer-out.txt Output matches baseline: 'MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer-rp.txt Output matches baseline: 'MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer.txt Output matches baseline: 'MultiClassNaiveBayes\MultiClassNaiveBayes-CV-breast-cancer.txt' Test MulticlassNaiveBayes: completed normally: passed Microsoft.ML.RunTests.TestPredictors.WeightingFastForestClassificationPredictorsTest [SKIP] Need CoreTLC specific baseline update Microsoft.ML.RunTests.TestPredictors.WeightingFastForestRegressionPredictorsTest [SKIP] Need CoreTLC specific baseline update Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesVotingCombinerTest with memory usage 126,455,808.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesVotingCombinerTest [PASS] Output: Running 'WeightedEnsemble' on 'breast-cancer' Starting test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiStackCombinerTest Running as: TrainTest tr=WeightedEnsemble{nm=20 oc=Voting tp=-} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer.txt} loader=Text{col=Label:BL:0 col=Features:R4:1-9} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=WeightedEnsemble{nm=20 oc=Voting tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer.txt loader=Text{col=Label:BL:0 col=Features:R4:1-9} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-model.zip seed=1 Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 20 learners for the batch 1 Beginning training model 1 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 26 instances with missing features during training (over 1 iterations; 26 inst/iter) Trainer 1 of 20 finished in 00:00:00.0009088 Beginning training model 2 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 2 of 20 finished in 00:00:00.0009207 Beginning training model 3 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 11 instances with missing features during training (over 1 iterations; 11 inst/iter) Trainer 3 of 20 finished in 00:00:00.0002998 Beginning training model 4 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 4 of 20 finished in 00:00:00.0002829 Beginning training model 5 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 5 of 20 finished in 00:00:00.0002978 Beginning training model 6 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 6 of 20 finished in 00:00:00.0002799 Beginning training model 7 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 7 of 20 finished in 00:00:00.0002643 Beginning training model 8 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 8 of 20 finished in 00:00:00.0002682 Beginning training model 9 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 15 instances with missing features during training (over 1 iterations; 15 inst/iter) Trainer 9 of 20 finished in 00:00:00.0002792 Beginning training model 10 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 10 of 20 finished in 00:00:00.0002710 Beginning training model 11 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 11 of 20 finished in 00:00:00.0002599 Beginning training model 12 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 13 instances with missing features during training (over 1 iterations; 13 inst/iter) Trainer 12 of 20 finished in 00:00:00.0003001 Beginning training model 13 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 14 instances with missing features during training (over 1 iterations; 14 inst/iter) Trainer 13 of 20 finished in 00:00:00.0002723 Beginning training model 14 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 17 instances with missing features during training (over 1 iterations; 17 inst/iter) Trainer 14 of 20 finished in 00:00:00.0003483 Beginning training model 15 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 24 instances with missing features during training (over 1 iterations; 24 inst/iter) Trainer 15 of 20 finished in 00:00:00.0003163 Beginning training model 16 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 16 of 20 finished in 00:00:00.0002866 Beginning training model 17 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 12 instances with missing features during training (over 1 iterations; 12 inst/iter) Trainer 17 of 20 finished in 00:00:00.0002625 Beginning training model 18 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 19 instances with missing features during training (over 1 iterations; 19 inst/iter) Trainer 18 of 20 finished in 00:00:00.0003289 Beginning training model 19 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 23 instances with missing features during training (over 1 iterations; 23 inst/iter) Trainer 19 of 20 finished in 00:00:00.0025404 Beginning training model 20 of 20 Warning: Training data does not support shuffling, so ignoring request to shuffle Warning: Skipped 18 instances with missing features during training (over 1 iterations; 18 inst/iter) Trainer 20 of 20 finished in 00:00:00.0003367 Training calibrator. Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.129466 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.861385 AUC: 0.992339 OVERALL RESULTS --------------------------------------- AUC: 0.992339 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.129466 (0.0000) Log-loss reduction: 0.861385 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.963675 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.078 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Voting-TrainTest-breast-cancer-out.txt Output matches baseline: 'WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Voting-TrainTest-breast-cancer-rp.txt Output matches baseline: 'WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsemble\win-arm\WE-Voting-TrainTest-breast-cancer.txt Output matches baseline: 'WeightedEnsemble\WE-Voting-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsemble\WE-Voting-TrainTest-breast-cancer-model.zip seed=1 Warning: The predictor produced non-finite prediction values on 16 instances during testing. Possible causes: abnormal data or the predictor is numerically unstable. TEST POSITIVE RATIO: 0.3499 (239.0/(239.0+444.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 231 | 8 | 0.9665 negative || 10 | 434 | 0.9775 ||====================== Precision || 0.9585 | 0.9819 | OVERALL 0/1 ACCURACY: 0.973646 LOG LOSS/instance: 0.129466 Test-set entropy (prior Log-Loss/instance): 0.934003 LOG-LOSS REDUCTION (RIG): 0.861385 AUC: 0.992339 OVERALL RESULTS --------------------------------------- AUC: 0.992339 (0.0000) Accuracy: 0.973646 (0.0000) Positive precision: 0.958506 (0.0000) Positive recall: 0.966527 (0.0000) Negative precision: 0.981900 (0.0000) Negative recall: 0.977477 (0.0000) Log-loss: 0.129466 (0.0000) Log-loss reduction: 0.861385 (0.0000) F1 Score: 0.962500 (0.0000) AUPRC: 0.963675 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.037 Suffix of length 34 compared against sequence of length 118 Test EnsemblesVotingCombinerTest: completed normally: passed Test EnsemblesVotingCombinerTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiStackCombinerTest with memory usage 130,801,664.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.EnsemblesMultiStackCombinerTest [PASS] Output: Running 'WeightedEnsembleMulticlass' on 'iris' Starting test: Microsoft.ML.RunTests.TestPredictors.GamBinaryClassificationTest Running as: TrainTest tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} data=C:\h\w\A567095C\p\test\data\iris.txt seed=1 test=C:\h\w\A567095C\p\test\data\iris.txt xf=Term{col=Label} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\iris.txt tr=WeightedEnsembleMulticlass{bp=mlr{t-} nm=5 oc=MultiStacking{bp=mlr{t-}} tp=-} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-model.zip seed=1 xf=Term{col=Label} Automatically adding a MinMax normalization transform, use 'norm=Warn' or 'norm=No' to turn this behavior off. Training 5 learners for the batch 1 Beginning training model 1 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 10 of 15 weights. Trainer 1 of 5 finished in 00:00:00.0103185 Beginning training model 2 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 12 of 15 weights. Trainer 2 of 5 finished in 00:00:00.0118079 Beginning training model 3 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 3 of 5 finished in 00:00:00.0091746 Beginning training model 4 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 11 of 15 weights. Trainer 4 of 5 finished in 00:00:00.0095467 Beginning training model 5 of 5 Beginning optimization num vars: 15 improvement criterion: Mean Improvement L1 regularization selected 13 of 15 weights. Trainer 5 of 5 finished in 00:00:00.0120785 The number of instances used for stacking trainer is 43 Warning: The trainer specified for stacking wants normalization, but we do not currently allow this. Beginning optimization num vars: 48 improvement criterion: Mean Improvement L1 regularization selected 26 of 48 weights. Not training a calibrator because it is not needed. Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 35 | 15 | 0.7000 2 || 0 | 0 | 50 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.7692 | Accuracy(micro-avg): 0.900000 Accuracy(macro-avg): 0.900000 Log-loss: 0.430565 Log-loss reduction: 0.608083 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.900000 (0.0000) Accuracy(macro-avg): 0.900000 (0.0000) Log-loss: 0.430565 (0.0000) Log-loss reduction: 0.608083 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.101 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Stacking-TrainTest-iris-out.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Stacking-TrainTest-iris-rp.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\WeightedEnsembleMulticlass\win-arm\WE-Stacking-TrainTest-iris.txt Output matches baseline: 'WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris.txt data=C:\h\w\A567095C\p\test\data\iris.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\WeightedEnsembleMulticlass\WE-Stacking-TrainTest-iris-model.zip seed=1 Confusion table ||======================== PREDICTED || 0 | 1 | 2 | Recall TRUTH ||======================== 0 || 50 | 0 | 0 | 1.0000 1 || 0 | 35 | 15 | 0.7000 2 || 0 | 0 | 50 | 1.0000 ||======================== Precision ||1.0000 |1.0000 |0.7692 | Accuracy(micro-avg): 0.900000 Accuracy(macro-avg): 0.900000 Log-loss: 0.430565 Log-loss reduction: 0.608083 OVERALL RESULTS --------------------------------------- Accuracy(micro-avg): 0.900000 (0.0000) Accuracy(macro-avg): 0.900000 (0.0000) Log-loss: 0.430565 (0.0000) Log-loss reduction: 0.608083 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:16 PM Time elapsed(s): 0.02 Suffix of length 27 compared against sequence of length 67 Test EnsemblesMultiStackCombinerTest: completed normally: passed Test EnsemblesMultiStackCombinerTest is using win-arm configuration specific baselines. Finished test: Microsoft.ML.RunTests.TestPredictors.GamBinaryClassificationTest with memory usage 131,284,992.00 and max memory usage 136,867,840.00 Microsoft.ML.RunTests.TestPredictors.GamBinaryClassificationTest [PASS] Output: Starting test: Microsoft.ML.RunTests.TestPredictors.RegressorOlsTestOne Running 'BinaryClassificationGamTrainer' on 'breast-cancer' Running as: TrainTest tr=BinaryClassificationGamTrainer data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=BinaryClassificationGamTrainer dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise Warning: Skipped 16 instances with missing features during training Processed 683 instances Binning and forming Feature objects Starting to train ... Training calibrator. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 9 | 0.9627 negative || 12 | 446 | 0.9738 ||====================== Precision || 0.9508 | 0.9802 | OVERALL 0/1 ACCURACY: 0.969957 LOG LOSS/instance: 0.113509 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.877858 AUC: 0.994972 OVERALL RESULTS --------------------------------------- AUC: 0.994972 (0.0000) Accuracy: 0.969957 (0.0000) Positive precision: 0.950820 (0.0000) Positive recall: 0.962656 (0.0000) Negative precision: 0.980220 (0.0000) Negative recall: 0.973799 (0.0000) Log-loss: 0.113509 (0.0000) Log-loss reduction: 0.877858 (0.0000) F1 Score: 0.956701 (0.0000) AUPRC: 0.989577 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:17 PM Time elapsed(s): 0.73 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt Output matches baseline: 'BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt Output matches baseline: 'BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt Output matches baseline: 'BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainer-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 9 | 0.9627 negative || 12 | 446 | 0.9738 ||====================== Precision || 0.9508 | 0.9802 | OVERALL 0/1 ACCURACY: 0.969957 LOG LOSS/instance: 0.113509 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.877858 AUC: 0.994972 OVERALL RESULTS --------------------------------------- AUC: 0.994972 (0.0000) Accuracy: 0.969957 (0.0000) Positive precision: 0.950820 (0.0000) Positive recall: 0.962656 (0.0000) Negative precision: 0.980220 (0.0000) Negative recall: 0.973799 (0.0000) Log-loss: 0.113509 (0.0000) Log-loss reduction: 0.877858 (0.0000) F1 Score: 0.956701 (0.0000) AUPRC: 0.989577 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:17 PM Time elapsed(s): 0.012 Suffix of length 33 compared against sequence of length 42 Running 'BinaryClassificationGamTrainer' on 'breast-cancer' Running as: TrainTest tr=BinaryClassificationGamTrainer{dt+} data=C:\h\w\A567095C\p\test\data\breast-cancer.txt seed=1 test=C:\h\w\A567095C\p\test\data\breast-cancer.txt out={C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt} maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\breast-cancer.txt tr=BinaryClassificationGamTrainer{dt+} dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt out=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-model.zip seed=1 Not adding a normalizer. Making per-feature arrays Changing data from row-wise to column-wise on disk Warning: 16 of 699 examples will be skipped due to missing feature values Processed 683 instances Binning and forming Feature objects Starting to train ... Training calibrator. TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 9 | 0.9627 negative || 12 | 446 | 0.9738 ||====================== Precision || 0.9508 | 0.9802 | OVERALL 0/1 ACCURACY: 0.969957 LOG LOSS/instance: 0.113509 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.877858 AUC: 0.994972 OVERALL RESULTS --------------------------------------- AUC: 0.994972 (0.0000) Accuracy: 0.969957 (0.0000) Positive precision: 0.950820 (0.0000) Positive recall: 0.962656 (0.0000) Negative precision: 0.980220 (0.0000) Negative recall: 0.973799 (0.0000) Log-loss: 0.113509 (0.0000) Log-loss reduction: 0.877858 (0.0000) F1 Score: 0.956701 (0.0000) AUPRC: 0.989577 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:17 PM Time elapsed(s): 0.509 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt Output matches baseline: 'BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-out.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt Output matches baseline: 'BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-rp.txt' Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt Output matches baseline: 'BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt' maml.exe Test dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer.txt data=C:\h\w\A567095C\p\test\data\breast-cancer.txt in=C:\h\w\A567095C\w\AD180968\e\TestOutput\BinaryClassificationGamTrainer\BinaryClassificationGamTrainerDiskTranspose-TrainTest-breast-cancer-model.zip seed=1 TEST POSITIVE RATIO: 0.3448 (241.0/(241.0+458.0)) Confusion table ||====================== PREDICTED || positive | negative | Recall TRUTH ||====================== positive || 232 | 9 | 0.9627 negative || 12 | 446 | 0.9738 ||====================== Precision || 0.9508 | 0.9802 | OVERALL 0/1 ACCURACY: 0.969957 LOG LOSS/instance: 0.113509 Test-set entropy (prior Log-Loss/instance): 0.929318 LOG-LOSS REDUCTION (RIG): 0.877858 AUC: 0.994972 OVERALL RESULTS --------------------------------------- AUC: 0.994972 (0.0000) Accuracy: 0.969957 (0.0000) Positive precision: 0.950820 (0.0000) Positive recall: 0.962656 (0.0000) Negative precision: 0.980220 (0.0000) Negative recall: 0.973799 (0.0000) Log-loss: 0.113509 (0.0000) Log-loss reduction: 0.877858 (0.0000) F1 Score: 0.956701 (0.0000) AUPRC: 0.989577 (0.0000) --------------------------------------- Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:17 PM Time elapsed(s): 0.011 Suffix of length 33 compared against sequence of length 42 Test GamBinaryClassificationTest: completed normally: passed Microsoft.ML.RunTests.TestPredictors.RegressorOlsTestOne [FAIL] System.AggregateException : One or more errors occurred. (Value cannot be null. (Parameter 'input')) (Assert.Equal() Failure: Values differ Expected: 0 Actual: 1) ---- System.ArgumentNullException : Value cannot be null. (Parameter 'input') ---- Assert.Equal() Failure: Values differ Expected: 0 Actual: 1 Stack Trace: ----- Inner Stack Trace #1 (System.ArgumentNullException) ----- at System.Text.RegularExpressions.ThrowHelper.ThrowArgumentNullException(ExceptionArgument arg) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs(556,0): at Microsoft.ML.RunTests.BaseTestBaseline.CheckEqualityFromPathsCore(String relPath, String basePath, String outPath, Int32 skip, Int32 digitsOfPrecision, NumberParseOption parseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs(397,0): at Microsoft.ML.RunTests.BaseTestBaseline.CheckEqualityNormalized(String dir, String name, String nameBase, Int32 digitsOfPrecision, NumberParseOption parseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs(213,0): at Microsoft.ML.RunTests.BaseTestPredictors.Run(RunContext ctx, Int32 digitsOfPrecision, NumberParseOption parseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs(396,0): at Microsoft.ML.RunTests.BaseTestPredictors.Run_TrainTest(PredictorAndArgs predictor, TestDataset dataset, String[] extraSettings, String extraTag, Boolean expectFailure, Boolean summary, Boolean saveAsIni, Int32 digitsOfPrecision, NumberParseOption parseOption) D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs(1201,0): at Microsoft.ML.RunTests.TestPredictors.RegressorOlsTestOne() at System.RuntimeMethodHandle.InvokeMethod(Object target, Void** arguments, Signature sig, Boolean isConstructor) at System.Reflection.MethodBaseInvoker.InvokeWithNoArgs(Object obj, BindingFlags invokeAttr) ----- Inner Stack Trace #2 (Xunit.Sdk.EqualException) ----- D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs(200,0): at Microsoft.ML.RunTests.BaseTestBaseline.Cleanup() D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestClass.cs(80,0): at Microsoft.ML.TestFramework.BaseTestClass.System.IDisposable.Dispose() /_/src/xunit.execution/Extensions/ReflectionAbstractionExtensions.cs(79,0): at ReflectionAbstractionExtensions.DisposeTestClass(ITest test, Object testClass, IMessageBus messageBus, ExecutionTimer timer, CancellationTokenSource cancellationTokenSource) Output: Running 'OLS' on 'generatedRegressionDataset' Running as: TrainTest tr=OLS data=C:\h\w\A567095C\p\test\data\generated_regression_dataset.csv seed=1 test=C:\h\w\A567095C\p\test\data\generated_regression_dataset.csv loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} out={C:\h\w\A567095C\w\AD180968\e\TestOutput\OLS\OLS-TrainTest-generatedRegressionDataset-model.zip} dout={C:\h\w\A567095C\w\AD180968\e\TestOutput\OLS\OLS-TrainTest-generatedRegressionDataset.txt} norm=no maml.exe TrainTest test=C:\h\w\A567095C\p\test\data\generated_regression_dataset.csv tr=OLS norm=No dout=C:\h\w\A567095C\w\AD180968\e\TestOutput\OLS\OLS-TrainTest-generatedRegressionDataset.txt loader=Text{col=Label:R4:11 col=Features:R4:0-10 sep=; header+} data=C:\h\w\A567095C\p\test\data\generated_regression_dataset.csv out=C:\h\w\A567095C\w\AD180968\e\TestOutput\OLS\OLS-TrainTest-generatedRegressionDataset-model.zip seed=1 Not adding a normalizer. Trainer solving for 12 parameters across 500 examples Physical memory usage(MB): 130 Virtual memory usage(MB): 2366128 08/11/2026 13:18:18 PM Time elapsed(s): 0.006 ***** Unexpected failure. Please refer to https://aka.ms/MLNetIssue to file an issue with details ***** ***** Error log has been saved to 'C:\h\w\A567095C\t\TLC\Error_20260811_131818_1e3ab920-cade-46f5-92a0-1517c8749185.log', please refer to https://aka.ms/MLNetIssue to file an issue with details ***** ===== Begin detailed dump ===== (1) Unexpected exception: Unable to find an entry point named 'LAPACKE_dpptrf' in DLL 'MklImports'., 'System.EntryPointNotFoundException' at Microsoft.ML.Trainers.OlsTrainer.Mkl.PptrfInternal(Layout layout, UpLo uplo, Int32 n, Double[] ap) at Microsoft.ML.Trainers.OlsTrainer.Mkl.Pptrf(Layout layout, UpLo uplo, Int32 n, Double[] ap) in D:\a\_work\1\s\src\Microsoft.ML.Mkl.Components\OlsLinearRegression.cs:line 568 at Microsoft.ML.Trainers.OlsTrainer.ComputeMklRegression(IChannel ch, Factory cursorFactory, Int32 m, Double[]& beta, Double[] xtx, Int64& n, Double& yMean) in D:\a\_work\1\s\src\Microsoft.ML.Mkl.Components\OlsLinearRegression.cs:line 392 at Microsoft.ML.Trainers.OlsTrainer.TrainCore(IChannel ch, Factory cursorFactory, Int32 featureCount) in D:\a\_work\1\s\src\Microsoft.ML.Mkl.Components\OlsLinearRegression.cs:line 439 at Microsoft.ML.Trainers.OlsTrainer.TrainModelCore(TrainContext context) in D:\a\_work\1\s\src\Microsoft.ML.Mkl.Components\OlsLinearRegression.cs:line 178 at Microsoft.ML.Trainers.TrainerEstimatorBase`2.Microsoft.ML.ITrainer.Train(TrainContext context) in D:\a\_work\1\s\src\Microsoft.ML.Data\Training\TrainerEstimatorBase.cs:line 100 at Microsoft.ML.Data.TrainUtils.TrainCore(IHostEnvironment env, IChannel ch, RoleMappedData data, ITrainer trainer, RoleMappedData validData, IComponentFactory`1 calibrator, Int32 maxCalibrationExamples, Nullable`1 cacheData, IPredictor inputPredictor, RoleMappedData testData) in D:\a\_work\1\s\src\Microsoft.ML.Data\Commands\TrainCommand.cs:line 280 at Microsoft.ML.Data.TrainTestCommand.RunCore(IChannel ch, String cmd) in D:\a\_work\1\s\src\Microsoft.ML.Data\Commands\TrainTestCommand.cs:line 186 at Microsoft.ML.Data.TrainTestCommand.Run() in D:\a\_work\1\s\src\Microsoft.ML.Data\Commands\TrainTestCommand.cs:line 108 at Microsoft.ML.Tools.Maml.MainCore(IHostEnvironment env, String args, Boolean alwaysPrintStacktrace) in D:\a\_work\1\s\src\Microsoft.ML.Maml\MAML.cs:line 130 ====== End detailed dump ===== *** Predictor returned -2 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OLS\OLS-TrainTest-generatedRegressionDataset-out.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OLS\OLS-TrainTest-generatedRegressionDataset-out.txt *** Failure #1: Output and baseline mismatch at line 4, expected 'Coefficient of determination R2 = %Number%, or %Number% (adjusted)' but got 'Physical memory usage(MB): %Number%' : 'OLS\OLS-TrainTest-generatedRegressionDataset-out.txt' Void Fail(System.String, System.Object[]) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 244 Boolean CheckEqualityFromPathsCore(System.String, System.String, System.String, Int32, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 566 Boolean CheckEqualityCore(System.String, System.String, System.String, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestBaseline.cs 429 Void Run(RunContext, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 169 Void Run_TrainTest(Microsoft.ML.RunTests.PredictorAndArgs, Microsoft.ML.TestFrameworkCommon.TestDataset, System.String[], System.String, Boolean, Boolean, Boolean, Int32, NumberParseOption) D:\a\_work\1\s\test\Microsoft.ML.TestFramework\BaseTestPredictorsMaml.cs 396 Void RegressorOlsTestOne() D:\a\_work\1\s\test\Microsoft.ML.Predictor.Tests\TestPredictors.cs 1201 System.Object InvokeMethod(System.Object, Void**, System.Signature, Boolean) 0 System.Object InvokeWithNoArgs(System.Object, System.Reflection.BindingFlags) 0 System.Object CallTestMethod(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 149 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 256 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task b__1() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/ExecutionTimer.cs 48 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task AggregateAsync(System.Func`1[System.Threading.Tasks.Task]) 0 System.Threading.Tasks.Task b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 215 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 90 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task RunAsync(System.Func`1[System.Threading.Tasks.Task]) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 214 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) 0 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(System.Object) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestInvoker.cs 112 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 180 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Decimal] b__46_0() 0 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[T] RunAsync[T](System.Func`1[System.Threading.Tasks.Task`1[T]]) 0 System.Threading.Tasks.Task`1[System.Decimal] RunAsync() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestInvoker.cs 163 System.Threading.Tasks.Task`1[System.Decimal] InvokeTestMethodAsync(Xunit.Sdk.ExceptionAggregator) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 88 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestRunner.cs 70 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] InvokeTestAsync(Xunit.Sdk.ExceptionAggregator) 0 System.Threading.Tasks.Task`1[System.Tuple`2[System.Decimal,System.String]] b__0() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void MoveNext() /_/src/xunit.core/Sdk/ExceptionAggregator.cs 107 Void Start[TStateMachine](TStateMachine ByRef) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestRunner.cs 149 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 136 Void Start[TStateMachine](TStateMachine ByRef) 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestMethodRunner.cs 106 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestMethodsAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestClassRunner.cs 175 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassAsync(Xunit.Abstractions.ITestClass, Xunit.Abstractions.IReflectionTypeInfo, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase]) /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestCollectionRunner.cs 185 Void MoveNext() 0 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestClassesAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/TestCollectionRunner.cs 101 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunAsync() 0 Void MoveNext() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 346 Void Start[TStateMachine](TStateMachine ByRef) 0 Void Start[TStateMachine](TStateMachine ByRef) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] RunTestCollectionAsync(Xunit.Sdk.IMessageBus, Xunit.Abstractions.ITestCollection, System.Collections.Generic.IEnumerable`1[Xunit.Sdk.IXunitTestCase], System.Threading.CancellationTokenSource) 0 System.Threading.Tasks.Task`1[Xunit.Sdk.RunSummary] b__2() /_/src/xunit.execution/Sdk/Frameworks/Runners/XunitTestAssemblyRunner.cs 250 Void InnerInvoke() 0 Void RunFromThreadPoolDispatchLoop(System.Threading.Thread, System.Threading.ExecutionContext, System.Threading.ContextCallback, System.Object) 0 Void ExecuteWithThreadLocal(System.Threading.Tasks.Task ByRef, System.Threading.Thread) 0 Boolean Dispatch() 0 Void WorkerThreadStart() 0 Comparing C:\h\w\A567095C\w\AD180968\e\TestOutput\OLS\OLS-TrainTest-generatedRegressionDataset-rp.txt and C:\h\w\A567095C\p\test\BaselineOutput\Common\OLS\OLS-TrainTest-generatedRegressionDataset-rp.txt Test RegressorOlsTestOne: aborted: failed Microsoft.ML.RunTests.TestPredictors.FastTreeRankingTest [SKIP] Need CoreTLC specific baseline update Finished: Microsoft.ML.Predictor.Tests === TEST EXECUTION SUMMARY === Microsoft.ML.Predictor.Tests Total: 111, Errors: 0, Failed: 2, Skipped: 55, Time: 31.184s ----- end Tue 08/11/2026 13:18:18.12 ----- exit code 1 ---------------------------------------------------------- C:\h\scripts\helix-scripts\azure\__init__.py:5: UserWarning: pkg_resources is deprecated as an API. See https://setuptools.pypa.io/en/latest/pkg_resources.html. The pkg_resources package is slated for removal as early as 2025-11-30. Refrain from using this package or pin to Setuptools<81. import pkg_resources 2026-08-11T13:18:30.646Z INFO run.py managed_identity(151) __init__ ManagedIdentityCredential will use IMDS with client_id: 3423231b-6b9e-4177-8817-76276b647404 2026-08-11T13:18:30.646Z INFO run.py azure_utils(32) get_credential_and_access_token VMSSManagedIdentity VmssMIClientIdForUpload Auth type will be used 2026-08-11T13:18:30.647Z INFO run.py _universal(535) on_request Request URL: 'http://169.254.169.254/metadata/identity/oauth2/token?api-version=2018-02-01&resource=REDACTED&client_id=REDACTED' Request method: 'GET' Request headers: 'Metadata': 'REDACTED' 'x-client-SKU': 'REDACTED' 'x-client-Ver': 'REDACTED' 'x-ms-client-request-id': '2cc222d7-1654-4e2b-9b72-591559064888' 'User-Agent': 'azsdk-python-identity/1.25.3 Python/3.13.13 (Windows-11-10.0.26100-SP0)' No body was attached to the request 2026-08-11T13:18:30.658Z INFO run.py _universal(581) on_response Response status: 200 Response headers: 'Content-Type': 'application/json; charset=utf-8' 'Server': 'IMDS/150.870.65.2118' 'x-ms-request-id': '2cc222d7-1654-4e2b-9b72-591559064888' 'Date': 'Tue, 11 Aug 2026 13:18:29 GMT' 'Content-Length': '2529' 2026-08-11T13:18:30.659Z INFO run.py msal_managed_identity_client(165) _get_token_base ImdsCredential.get_token succeeded 2026-08-11T13:18:30.659Z INFO run.py decorators(24) wrapper ManagedIdentityCredential.get_token succeeded 2026-08-11T13:18:30.659Z INFO run.py azure_utils(64) get_credential_and_access_token Credentials are valid 2026-08-11T13:18:30.660Z INFO run.py msal_managed_identity_client(165) _get_token_base ImdsCredential.get_token_info succeeded 2026-08-11T13:18:30.660Z INFO run.py decorators(24) wrapper ManagedIdentityCredential.get_token_info succeeded 2026-08-11T13:18:30.974Z INFO run.py run(48) main Beginning reading of test results. 2026-08-11T13:18:30.974Z INFO run.py __init__(42) read_results Searching 'C:\h\w\A567095C\w\AD180968\e' for test results files 2026-08-11T13:18:30.977Z INFO run.py __init__(48) read_results Found results file C:\h\w\A567095C\w\AD180968\e\testResults.xml with format xunit 2026-08-11T13:18:30.990Z INFO run.py __init__(42) read_results Searching 'C:\h\w\A567095C\w\AD180968\uploads' for test results files 2026-08-11T13:18:30.991Z INFO run.py packing_test_reporter(30) report_results Packing 111 test reports to 'C:\h\w\A567095C\w\AD180968\e\__test_report.json' 2026-08-11T13:18:30.992Z INFO run.py packing_test_reporter(33) report_results Packed 141870 bytes ['Microsoft.ML.Predictor.Tests' END OF WORK ITEM LOG: Command exited with 0]