Output in the output widow is too verbose

[rustd]

We should be just showing errors if there are any not the entire stream of logs.

Inferring Columns ... Creating Data loader ... Loading data ... Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification For further learning check: https://aka.ms/mlnet-cli | Trainer Accuracy AUC AUPRC F1-score Duration #Iteration | [Source=AutoML, Kind=Trace] Channel started [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ [Source=AutoML, Kind=Trace] 1 0.84961431656896 00:00:04.3053446 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ |1 AveragedPerceptronBinary 0.8496 0.9082 0.7699 0.6816 4.3 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+ [Source=AutoML, Kind=Trace] 2 0.849768589941376 00:00:02.0374479 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+ |2 SdcaLogisticRegressionBinary 0.8498 0.9057 0.7631 0.6797 2.0 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{} cache=-

===============================================Experiment Results=================================================

| Summary |

|ML Task: binary-classification | |Dataset: d09517a5-ae7f-4151-a22f-bd40c8f26402.tsv | |Label : Label | |Total experiment time : 11.19 Secs | |Total number of models explored: 2 |

| Top 2 models explored |

| Trainer Accuracy AUC AUPRC F1-score Duration #Iteration | |1 SdcaLogisticRegressionBinary 0.8498 0.9057 0.7631 0.6797 2.0 2 | |2 AveragedPerceptronBinary 0.8496 0.9082 0.7699 0.6816 4.3 1 |

Generated trained model for consumption: C:\Users\pranavra\AppData\Local\Temp\MLVSTools\ConsoleApp23ML\ConsoleApp23ML.Model\MLModel.zip Retrieving best pipeline ... Generated C# code for model consumption: C:\Users\pranavra\AppData\Local\Temp\MLVSTools\ConsoleApp23ML\ConsoleApp23ML.ConsoleApp Check out log file for more information: C:\Users\pranavra\AppData\Local\Temp\MLVSTools\ConsoleApp23ML\logs\debug_log.txt

Inferring Columns ... Creating Data loader ... Loading data ... Exploring multiple ML algorithms and settings to find you the best model for ML task: binary-classification For further learning check: https://aka.ms/mlnet-cli | Trainer Accuracy AUC AUPRC F1-score Duration #Iteration | [Source=AutoML, Kind=Trace] Channel started [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ [Source=AutoML, Kind=Trace] 1 0.851342178340018 00:00:04.0986354 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=AveragedPerceptronBinary{} cache=+ |1 AveragedPerceptronBinary 0.8513 0.9083 0.7698 0.6651 4.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+ [Source=AutoML, Kind=Trace] 2 0.849737735266893 00:00:02.4605527 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SdcaLogisticRegressionBinary{} cache=+ |2 SdcaLogisticRegressionBinary 0.8497 0.9062 0.7653 0.6653 2.5 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{} cache=- [Source=AutoML, Kind=Trace] 3 0.879851897562481 00:00:04.0691632 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{} cache=- |3 LightGbmBinary 0.8799 0.9380 0.8487 0.7309 4.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+ [Source=AutoML, Kind=Trace] 4 0.845418080839247 00:00:02.0240498 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SymbolicSgdLogisticRegressionBinary{} cache=+ |4 SymbolicSgdLogisticRegressionBinary 0.8454 0.9006 0.7445 0.6413 2.0 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+ [Source=AutoML, Kind=Trace] 5 0.83992594878124 00:00:02.1414796 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=LinearSvmBinary{} cache=+ |5 LinearSvmBinary 0.8399 0.8905 0.7260 0.6292 2.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{} cache=- [Source=AutoML, Kind=Trace] 6 0.885251465597038 00:00:05.8161207 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{} cache=- |6 FastTreeBinary 0.8853 0.9422 0.8586 0.7445 5.8 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=LbfgsLogisticRegressionBinary{} cache=+ [Source=AutoML, Kind=Trace] 7 0.851249614316569 00:00:05.1074821 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=LbfgsLogisticRegressionBinary{} cache=+ |7 LbfgsLogisticRegressionBinary 0.8512 0.9079 0.7686 0.6622 5.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{} cache=- [Source=AutoML, Kind=Trace] 8 0.855785251465597 00:00:06.6331844 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{} cache=- |8 FastForestBinary 0.8558 0.9039 0.7876 0.6600 6.6 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SgdCalibratedBinary{} cache=+ [Source=AutoML, Kind=Trace] 9 0.85029311940759 00:00:03.1319802 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} xf=Normalizing{ col=Features:Features} tr=SgdCalibratedBinary{} cache=+ |9 SgdCalibratedBinary 0.8503 0.9076 0.7680 0.6406 3.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:200, LearningRate:0.07131317, NumberOfLeaves:2, MinimumExampleCountPerLeaf:50, UseCategoricalSplit:False, HandleMissingValue:True, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:20, L2CategoricalRegularization:0.5, L2Regularization:1, L1Regularization:1} cache=- [Source=AutoML, Kind=Trace] 10 0.853131749460043 00:00:03.4028991 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:200, LearningRate:0.07131317, NumberOfLeaves:2, MinimumExampleCountPerLeaf:50, UseCategoricalSplit:False, HandleMissingValue:True, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:20, L2CategoricalRegularization:0.5, L2Regularization:1, L1Regularization:1} cache=- |10 LightGbmBinary 0.8531 0.9024 0.7784 0.6268 3.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:51, MinimumExampleCountPerLeaf:50, NumberOfTrees:20, LearningRate:0.1922826, Shrinkage:0.3451828} cache=- [Source=AutoML, Kind=Trace] 11 0.869330453563715 00:00:03.0538213 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:51, MinimumExampleCountPerLeaf:50, NumberOfTrees:20, LearningRate:0.1922826, Shrinkage:0.3451828} cache=- |11 FastTreeBinary 0.8693 0.9207 0.8158 0.6979 3.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:45, MinimumExampleCountPerLeaf:10, NumberOfTrees:20} cache=- [Source=AutoML, Kind=Trace] 12 0.86143165689602 00:00:02.9653915 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:45, MinimumExampleCountPerLeaf:10, NumberOfTrees:20} cache=- |12 FastForestBinary 0.8614 0.9131 0.8028 0.6707 3.0 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.2076601, NumberOfLeaves:5, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:100, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:1, L2CategoricalRegularization:0.5, L2Regularization:1, L1Regularization:0} cache=- [Source=AutoML, Kind=Trace] 13 0.872601049058932 00:00:03.3437532 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.2076601, NumberOfLeaves:5, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:100, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:1, L2CategoricalRegularization:0.5, L2Regularization:1, L1Regularization:0} cache=- |13 LightGbmBinary 0.8726 0.9301 0.8317 0.7095 3.3 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:38, MinimumExampleCountPerLeaf:10, NumberOfTrees:100, LearningRate:0.2157515, Shrinkage:0.08323191} cache=- [Source=AutoML, Kind=Trace] 14 0.86966985498303 00:00:07.4013933 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:38, MinimumExampleCountPerLeaf:10, NumberOfTrees:100, LearningRate:0.2157515, Shrinkage:0.08323191} cache=- |14 FastTreeBinary 0.8697 0.9209 0.8164 0.6977 7.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:83, MinimumExampleCountPerLeaf:1, NumberOfTrees:20} cache=- [Source=AutoML, Kind=Trace] 15 0.867602591792657 00:00:03.1772681 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:83, MinimumExampleCountPerLeaf:1, NumberOfTrees:20} cache=- |15 FastForestBinary 0.8676 0.9204 0.8175 0.6869 3.2 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:50, LearningRate:0.2889481, NumberOfLeaves:2, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:100, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:1, L2CategoricalRegularization:1, L2Regularization:0, L1Regularization:1} cache=- [Source=AutoML, Kind=Trace] 16 0.858222770749769 00:00:02.7410251 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:50, LearningRate:0.2889481, NumberOfLeaves:2, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:100, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:1, L2CategoricalRegularization:1, L2Regularization:0, L1Regularization:1} cache=- |16 LightGbmBinary 0.8582 0.9116 0.7921 0.6619 2.7 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:4, MinimumExampleCountPerLeaf:1, NumberOfTrees:500, LearningRate:0.03719681, Shrinkage:0.0502417} cache=- [Source=AutoML, Kind=Trace] 17 0.820456649182351 00:00:11.2030578 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:4, MinimumExampleCountPerLeaf:1, NumberOfTrees:500, LearningRate:0.03719681, Shrinkage:0.0502417} cache=- |17 FastTreeBinary 0.8205 0.8818 0.7476 0.4209 11.2 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:8, MinimumExampleCountPerLeaf:10, NumberOfTrees:20} cache=- [Source=AutoML, Kind=Trace] 18 0.847855600123419 00:00:02.4276524 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:8, MinimumExampleCountPerLeaf:10, NumberOfTrees:20} cache=- |18 FastForestBinary 0.8479 0.8913 0.7586 0.6235 2.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.05710052, NumberOfLeaves:56, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:10, MaximumCategoricalSplitPointCount:16, CategoricalSmoothing:1, L2CategoricalRegularization:0.5, L2Regularization:0.5, L1Regularization:0.5} cache=- [Source=AutoML, Kind=Trace] 19 0.884973773526689 00:00:04.6324741 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.05710052, NumberOfLeaves:56, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:10, MaximumCategoricalSplitPointCount:16, CategoricalSmoothing:1, L2CategoricalRegularization:0.5, L2Regularization:0.5, L1Regularization:0.5} cache=- |19 LightGbmBinary 0.8850 0.9438 0.8599 0.7435 4.6 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:42, MinimumExampleCountPerLeaf:10, NumberOfTrees:500, LearningRate:0.1590073, Shrinkage:1.024364} cache=- [Source=AutoML, Kind=Trace] 20 0.919654427645788 00:00:15.7825352 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:42, MinimumExampleCountPerLeaf:10, NumberOfTrees:500, LearningRate:0.1590073, Shrinkage:1.024364} cache=- |20 FastTreeBinary 0.9197 0.9712 0.9245 0.8265 15.8 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:15, MinimumExampleCountPerLeaf:10, NumberOfTrees:100} cache=- [Source=AutoML, Kind=Trace] 21 0.853070040111077 00:00:06.8671548 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:15, MinimumExampleCountPerLeaf:10, NumberOfTrees:100} cache=- |21 FastForestBinary 0.8531 0.8996 0.7775 0.6545 6.9 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:10, LearningRate:0.1073649, NumberOfLeaves:56, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:True, HandleMissingValue:True, MinimumExampleCountPerGroup:10, MaximumCategoricalSplitPointCount:32, CategoricalSmoothing:10, L2CategoricalRegularization:1, L2Regularization:0.5, L1Regularization:1} cache=- [Source=AutoML, Kind=Trace] 22 0.861462511570503 00:00:03.9023647 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:10, LearningRate:0.1073649, NumberOfLeaves:56, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:True, HandleMissingValue:True, MinimumExampleCountPerGroup:10, MaximumCategoricalSplitPointCount:32, CategoricalSmoothing:10, L2CategoricalRegularization:1, L2Regularization:0.5, L1Regularization:1} cache=- |22 LightGbmBinary 0.8615 0.9231 0.8185 0.6380 3.9 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:5, MinimumExampleCountPerLeaf:50, NumberOfTrees:100, LearningRate:0.03725918, Shrinkage:0.03750009} cache=- [Source=AutoML, Kind=Trace] 23 0.820456649182351 00:00:05.3786412 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:5, MinimumExampleCountPerLeaf:50, NumberOfTrees:100, LearningRate:0.03725918, Shrinkage:0.03750009} cache=- |23 FastTreeBinary 0.8205 0.8452 0.7087 0.4209 5.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:36, MinimumExampleCountPerLeaf:10, NumberOfTrees:500} cache=- [Source=AutoML, Kind=Trace] 24 0.861153964825671 00:00:37.3478192 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:36, MinimumExampleCountPerLeaf:10, NumberOfTrees:500} cache=- |24 FastForestBinary 0.8612 0.9121 0.8015 0.6692 37.3 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.05174248, NumberOfLeaves:70, MinimumExampleCountPerLeaf:50, UseCategoricalSplit:True, HandleMissingValue:True, MinimumExampleCountPerGroup:10, MaximumCategoricalSplitPointCount:64, CategoricalSmoothing:1, L2CategoricalRegularization:0.1, L2Regularization:0.5, L1Regularization:0.5} cache=- [Source=AutoML, Kind=Trace] 25 0.886578216599815 00:00:08.3119204 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.05174248, NumberOfLeaves:70, MinimumExampleCountPerLeaf:50, UseCategoricalSplit:True, HandleMissingValue:True, MinimumExampleCountPerGroup:10, MaximumCategoricalSplitPointCount:64, CategoricalSmoothing:1, L2CategoricalRegularization:0.1, L2Regularization:0.5, L1Regularization:0.5} cache=- |25 LightGbmBinary 0.8866 0.9454 0.8628 0.7475 8.3 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:124, MinimumExampleCountPerLeaf:1, NumberOfTrees:500, LearningRate:0.03242409, Shrinkage:0.3104021} cache=- [Source=AutoML, Kind=Trace] 26 0.893643937056464 00:01:00.8210460 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:124, MinimumExampleCountPerLeaf:1, NumberOfTrees:500, LearningRate:0.03242409, Shrinkage:0.3104021} cache=- |26 FastTreeBinary 0.8936 0.9490 0.8747 0.7634 60.8 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:3, MinimumExampleCountPerLeaf:50, NumberOfTrees:20} cache=- [Source=AutoML, Kind=Trace] 27 0.79524838012959 00:00:03.1061657 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:3, MinimumExampleCountPerLeaf:50, NumberOfTrees:20} cache=- |27 FastForestBinary 0.7952 0.8314 0.6528 0.2643 3.1 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.1232464, NumberOfLeaves:64, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:False, HandleMissingValue:False, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:64, CategoricalSmoothing:20, L2CategoricalRegularization:5, L2Regularization:0.5, L1Regularization:0} cache=- |28 LightGbmBinary 0.8910 0.9505 0.8744 0.7588 6.6 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:3, MinimumExampleCountPerLeaf:10, NumberOfTrees:500, LearningRate:0.3015409, Shrinkage:0.7423708} cache=- [Source=AutoML, Kind=Trace] 28 0.890959580376427 00:00:06.5630086 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:100, LearningRate:0.1232464, NumberOfLeaves:64, MinimumExampleCountPerLeaf:20, UseCategoricalSplit:False, HandleMissingValue:False, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:64, CategoricalSmoothing:20, L2CategoricalRegularization:5, L2Regularization:0.5, L1Regularization:0} cache=- [Source=AutoML, Kind=Trace] 29 0.873680962665844 00:00:10.1837049 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:3, MinimumExampleCountPerLeaf:10, NumberOfTrees:500, LearningRate:0.3015409, Shrinkage:0.7423708} cache=- |29 FastTreeBinary 0.8737 0.9332 0.8388 0.7123 10.2 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:114, MinimumExampleCountPerLeaf:10, NumberOfTrees:500} cache=- [Source=AutoML, Kind=Trace] 30 0.869762419006479 00:00:52.6271161 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:114, MinimumExampleCountPerLeaf:10, NumberOfTrees:500} cache=- |30 FastForestBinary 0.8698 0.9234 0.8219 0.6964 52.6 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:10, LearningRate:0.05529442, NumberOfLeaves:9, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:200, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:20, L2CategoricalRegularization:1, L2Regularization:0.5, L1Regularization:1} cache=- [Source=AutoML, Kind=Trace] 31 0.814008022215366 00:00:03.6154244 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:10, LearningRate:0.05529442, NumberOfLeaves:9, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:200, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:20, L2CategoricalRegularization:1, L2Regularization:0.5, L1Regularization:1} cache=- |31 LightGbmBinary 0.8140 0.8944 0.7649 0.3761 3.6 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:41, MinimumExampleCountPerLeaf:1, NumberOfTrees:20, LearningRate:0.3701605, Shrinkage:0.2204921} cache=- [Source=AutoML, Kind=Trace] 32 0.868404813329219 00:00:03.9451119 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:41, MinimumExampleCountPerLeaf:1, NumberOfTrees:20, LearningRate:0.3701605, Shrinkage:0.2204921} cache=- |32 FastTreeBinary 0.8684 0.9205 0.8156 0.6940 3.9 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:3, MinimumExampleCountPerLeaf:1, NumberOfTrees:100} cache=- [Source=AutoML, Kind=Trace] 33 0.79515581610614 00:00:04.3137294 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:3, MinimumExampleCountPerLeaf:1, NumberOfTrees:100} cache=- |33 FastForestBinary 0.7952 0.8509 0.6871 0.2637 4.3 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:10, LearningRate:0.04638082, NumberOfLeaves:96, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:False, HandleMissingValue:True, MinimumExampleCountPerGroup:100, MaximumCategoricalSplitPointCount:16, CategoricalSmoothing:10, L2CategoricalRegularization:0.1, L2Regularization:0.5, L1Regularization:0.5} cache=- [Source=AutoML, Kind=Trace] 34 0.822369639000309 00:00:03.6002504 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:10, LearningRate:0.04638082, NumberOfLeaves:96, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:False, HandleMissingValue:True, MinimumExampleCountPerGroup:100, MaximumCategoricalSplitPointCount:16, CategoricalSmoothing:10, L2CategoricalRegularization:0.1, L2Regularization:0.5, L1Regularization:0.5} cache=- |34 LightGbmBinary 0.8224 0.9192 0.8140 0.4208 3.6 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:17, MinimumExampleCountPerLeaf:50, NumberOfTrees:20, LearningRate:0.05071186, Shrinkage:0.06548361} cache=- [Source=AutoML, Kind=Trace] 35 0.85251465597038 00:00:03.3629379 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:17, MinimumExampleCountPerLeaf:50, NumberOfTrees:20, LearningRate:0.05071186, Shrinkage:0.06548361} cache=- |35 FastTreeBinary 0.8525 0.8820 0.7580 0.6468 3.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:14, MinimumExampleCountPerLeaf:50, NumberOfTrees:500} cache=- [Source=AutoML, Kind=Trace] 36 0.852854057389695 00:00:21.9819411 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:14, MinimumExampleCountPerLeaf:50, NumberOfTrees:500} cache=- |36 FastForestBinary 0.8529 0.9001 0.7802 0.6532 22.0 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:150, LearningRate:0.06092425, NumberOfLeaves:123, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:10, L2CategoricalRegularization:10, L2Regularization:0.5, L1Regularization:0.5} cache=- [Source=AutoML, Kind=Trace] 37 0.89796359148411 00:00:09.3709814 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:150, LearningRate:0.06092425, NumberOfLeaves:123, MinimumExampleCountPerLeaf:10, UseCategoricalSplit:True, HandleMissingValue:False, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:8, CategoricalSmoothing:10, L2CategoricalRegularization:10, L2Regularization:0.5, L1Regularization:0.5} cache=- |37 LightGbmBinary 0.8980 0.9567 0.8875 0.7756 9.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:77, MinimumExampleCountPerLeaf:1, NumberOfTrees:500, LearningRate:0.2317246, Shrinkage:0.151768} cache=- [Source=AutoML, Kind=Trace] 38 0.904813329219377 00:00:32.0048375 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:77, MinimumExampleCountPerLeaf:1, NumberOfTrees:500, LearningRate:0.2317246, Shrinkage:0.151768} cache=- |38 FastTreeBinary 0.9048 0.9594 0.8965 0.7917 32.0 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:86, MinimumExampleCountPerLeaf:50, NumberOfTrees:100} cache=- [Source=AutoML, Kind=Trace] 39 0.867016352977476 00:00:10.4066351 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:86, MinimumExampleCountPerLeaf:50, NumberOfTrees:100} cache=- |39 FastForestBinary 0.8670 0.9196 0.8147 0.6911 10.4 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:50, LearningRate:0.112787, NumberOfLeaves:32, MinimumExampleCountPerLeaf:50, UseCategoricalSplit:True, HandleMissingValue:True, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:32, CategoricalSmoothing:20, L2CategoricalRegularization:10, L2Regularization:1, L1Regularization:1} cache=- [Source=AutoML, Kind=Trace] 40 0.878093181116939 00:00:04.8852570 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=LightGbmBinary{NumberOfIterations:50, LearningRate:0.112787, NumberOfLeaves:32, MinimumExampleCountPerLeaf:50, UseCategoricalSplit:True, HandleMissingValue:True, MinimumExampleCountPerGroup:50, MaximumCategoricalSplitPointCount:32, CategoricalSmoothing:20, L2CategoricalRegularization:10, L2Regularization:1, L1Regularization:1} cache=- |40 LightGbmBinary 0.8781 0.9364 0.8454 0.7268 4.9 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:13, MinimumExampleCountPerLeaf:10, NumberOfTrees:500, LearningRate:0.1822887, Shrinkage:0.08070181} cache=- [Source=AutoML, Kind=Trace] 41 0.871243443381672 00:00:25.2419240 xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastTreeBinary{NumberOfLeaves:13, MinimumExampleCountPerLeaf:10, NumberOfTrees:500, LearningRate:0.1822887, Shrinkage:0.08070181} cache=- |41 FastTreeBinary 0.8712 0.9280 0.8288 0.7039 25.2 0 | [Source=AutoML, Kind=Trace] Evaluating pipeline xf=OneHotEncoding{ col=Workclass:Workclass col=Education:Education col=MaritalStatus:MaritalStatus col=Occupation:Occupation col=Relationship:Relationship col=Race:Race col=Sex:Sex col=NativeCountry:NativeCountry} xf=ColumnConcatenating{ col=Features:Workclass,Education,MaritalStatus,Occupation,Relationship,Race,Sex,NativeCountry,AdultCensusId,Age,CapitalGain,CapitalLoss,HoursPerWeek} tr=FastForestBinary{NumberOfLeaves:17, MinimumExampleCountPerLeaf:10, NumberOfTrees:500} cache=-

[JakeRadMSFT]

I think it's great for debugging and having users report issues. Let's sync and see what we can do.

[rustd]

Won't fix