(* Content-type: application/vnd.wolfram.mathematica *) (*** Wolfram Notebook File ***) (* http://www.wolfram.com/nb *) (* CreatedBy='Mathematica 13.0' *) (*CacheID: 234*) (* Internal cache information: NotebookFileLineBreakTest NotebookFileLineBreakTest NotebookDataPosition[ 158, 7] NotebookDataLength[ 115157, 2417] NotebookOptionsPosition[ 111933, 2332] NotebookOutlinePosition[ 113697, 2384] CellTagsIndexPosition[ 113654, 2381] WindowTitle->TableToTrainingSet | Example Notebook WindowFrame->Normal*) (* Beginning of Notebook Content *) Notebook[{ Cell[CellGroupData[{ Cell[TextData[{ "Basic Examples", "\[NonBreakingSpace]", Cell["(2)", "ExampleCount"], "\[NonBreakingSpace]" }], "Subsection", TaggingRules->{}, CellID->462042388], Cell[TextData[{ "Prepare a matrix so that ", Cell[BoxData[ TagBox[ ButtonBox[ StyleBox["Classify", "SymbolsRefLink", ShowStringCharacters->True, FontFamily->"Source Sans Pro"], BaseStyle->Dynamic[ FEPrivate`If[ CurrentValue["MouseOver"], { "Link", FontColor -> 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1|>|>, "Output" -> <| "(f1f2)" -> <| "Weight" -> {0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125}, "Type" -> "NumericalVector"|>|>, "Processors" -> { MachineLearning`MLProcessor[ "Threads", <| "Input" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Output" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "ImputeMissing", <| "Invertibility" -> "Perfect", "Missing" -> "Imputed", "Input" -> <|"f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Fill" -> "", "Output" -> <|"f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Type" -> "Text", "Version" -> {12., 0}, "ID" -> 7263179337424669307|>], MachineLearning`MLProcessor[ "ImputeMissing", <| "Invertibility" -> "Perfect", "Missing" -> "Imputed", "Input" -> <|"f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Fill" -> "", "Output" -> <|"f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Type" -> "Text", "Version" -> {12., 0}, "ID" -> 114010418416267986|>]}, "Invertibility" -> "Perfect", "Missing" -> "Imputed"|>], MachineLearning`MLProcessor[ "Threads", <| "Input" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Output" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "ToLowerCase", <| "Invertibility" -> "Approximate", "Missing" -> "Allowed", "Input" -> <|"f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Version" -> {12., 0}, "ID" -> 8362939175913313252, "Output" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>|>], MachineLearning`MLProcessor[ "ToLowerCase", <| "Invertibility" -> "Approximate", "Missing" -> "Allowed", "Input" -> <|"f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Version" -> {12., 0}, "ID" -> 2888237905935310439, "Output" -> <| "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>|>]}, "Invertibility" -> "Approximate", "Missing" -> "Allowed"|>], MachineLearning`MLProcessor[ "Threads", <| "Input" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Output" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "RemoveDiacritics", <| "Invertibility" -> "Approximate", "Missing" -> "Allowed", "Input" -> <|"f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Version" -> {12., 0}, "ID" -> 1602110507922425163, "Output" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>|>], MachineLearning`MLProcessor[ "RemoveDiacritics", <| "Invertibility" -> "Approximate", "Missing" -> "Allowed", "Input" -> <|"f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Version" -> {12., 0}, "ID" -> 6549344891647408999, "Output" -> <| "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>|>]}, "Invertibility" -> "Approximate", "Missing" -> "Allowed"|>], MachineLearning`MLProcessor[ "Threads", <| "Input" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>, "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Output" -> <| "f1" -> <|"Type" -> "NumericalVector", "Weight" -> 1|>, "f2" -> <|"Type" -> "NumericalVector", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "Sequence", <| "Input" -> <|"f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Output" -> <| "f1" -> <|"Type" -> "NumericalVector", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "TextToNominalSequence", <| "Invertibility" -> "Perfect", "Missing" -> "Forbidden", "Input" -> <| "f1" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Method" -> "ToCharacterCode", "Version" -> {12., 0}, "ID" -> 7774961058889587341, "Output" -> <| "f1" -> <| "Type" -> "NominalSequence", "Weight" -> 1|>|>|>], MachineLearning`MLProcessor[ "NominalSequenceToTFIDFVector", <| "Invertibility" -> "Impossible", "Missing" -> "Forbidden", "Input" -> <| "f1" -> <|"Type" -> "NominalSequence", "Weight" -> 1|>|>, "Index" -> MachineLearning`SortedHashAssociation[<| "KeyHashes" -> {50, 51, 98, 106, 111, 113, 117, 118}, "Values" -> None, "DefaultValue" -> -1, "HashFunction" -> Function[{ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$}, Switch[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, { BlankSequence[String]}, Data`StringHash[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, "Murmur3-64"], PatternTest[ Blank[], MachineLearning`PackageScope`PackedArrayQ[#, Integer, 1]& ], MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, PatternTest[ Blank[], MachineLearning`PackageScope`PackedArrayQ[#, Integer, 2]& ], Block[{MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q}, MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = { Quotient[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^48], Quotient[ Mod[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^48], 2^32], Quotient[ Mod[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^32], 2^16], Mod[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^16]}; MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = Transpose[ MachineLearning`PackageScope`ToPackedArray[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q], {1, 3, 2}]; MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = Transpose[ Flatten[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q, 1]]; Data`StringHash[ FromCharacterCode[ Abs[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q]], "Murmur3-64"]], Blank[List], Map[Switch[#, Blank[String], Data`StringHash[#, "Murmur3-64"], PatternTest[ Blank[], Developer`MachineIntegerQ], #, { PatternTest[ BlankSequence[], Developer`MachineIntegerQ]}, Block[{MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q}, MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = { Quotient[#, 2^48], Quotient[ Mod[#, 2^48], 2^32], Quotient[ Mod[#, 2^32], 2^16], Mod[#, 2^16]}; MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = Flatten[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q]; Data`StringHash[ FromCharacterCode[ Abs[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q]], "Murmur3-64"]], Blank[], Data`StringHash[ ToString[#, InputForm] <> "Cn.i9)P$", "Murmur3-64"]]& , MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$]]], "Version" -> {12., 0}|>], "InverseDocumentFrequency" -> {-2.0794415416798357`, \ -2.0794415416798357`, -1.3862943611198906`, -1.3862943611198906`, \ -1.3862943611198906`, -1.3862943611198906`, -1.3862943611198906`, \ -1.3862943611198906`}, "Version" -> {12., 0}, "ID" -> 9090581535298543684, "Output" -> <| "f1" -> <| "Type" -> "NumericalVector", "Weight" -> 1|>|>|>]}, "Invertibility" -> "Impossible", "Missing" -> "Forbidden"|>], MachineLearning`MLProcessor[ "Sequence", <| "Input" -> <|"f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Output" -> <| "f2" -> <|"Type" -> "NumericalVector", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "TextToNominalSequence", <| "Invertibility" -> "Perfect", "Missing" -> "Forbidden", "Input" -> <| "f2" -> <|"Type" -> "Text", "Weight" -> 1|>|>, "Method" -> "ToCharacterCode", "Version" -> {12., 0}, "ID" -> 8721711979610692186, "Output" -> <| "f2" -> <| "Type" -> "NominalSequence", "Weight" -> 1|>|>|>], MachineLearning`MLProcessor[ "NominalSequenceToTFIDFVector", <| "Invertibility" -> "Impossible", "Missing" -> "Forbidden", "Input" -> <| "f2" -> <|"Type" -> "NominalSequence", "Weight" -> 1|>|>, "Index" -> MachineLearning`SortedHashAssociation[<| "KeyHashes" -> {50, 51, 99, 106, 111, 113, 117, 118}, "Values" -> None, "DefaultValue" -> -1, "HashFunction" -> Function[{ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$}, Switch[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, { BlankSequence[String]}, Data`StringHash[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, "Murmur3-64"], PatternTest[ Blank[], MachineLearning`PackageScope`PackedArrayQ[#, Integer, 1]& ], MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, PatternTest[ Blank[], MachineLearning`PackageScope`PackedArrayQ[#, Integer, 2]& ], Block[{MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q}, MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = { Quotient[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^48], Quotient[ Mod[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^48], 2^32], Quotient[ Mod[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^32], 2^16], Mod[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$, 2^16]}; MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = Transpose[ MachineLearning`PackageScope`ToPackedArray[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q], {1, 3, 2}]; MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = Transpose[ Flatten[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q, 1]]; Data`StringHash[ FromCharacterCode[ Abs[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q]], "Murmur3-64"]], Blank[List], Map[Switch[#, Blank[String], Data`StringHash[#, "Murmur3-64"], PatternTest[ Blank[], Developer`MachineIntegerQ], #, { PatternTest[ BlankSequence[], Developer`MachineIntegerQ]}, Block[{MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q}, MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = { Quotient[#, 2^48], Quotient[ Mod[#, 2^48], 2^32], Quotient[ Mod[#, 2^32], 2^16], Mod[#, 2^16]}; MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q = Flatten[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q]; Data`StringHash[ FromCharacterCode[ Abs[ MachineLearning`file152SortedHashAssociation`\ PackagePrivate`q]], "Murmur3-64"]], Blank[], Data`StringHash[ ToString[#, InputForm] <> "Cn.i9)P$", "Murmur3-64"]]& , MachineLearning`file152SortedHashAssociation`\ PackagePrivate`keys$]]], "Version" -> {12., 0}|>], "InverseDocumentFrequency" -> {-2.0794415416798357`, \ -2.0794415416798357`, -1.3862943611198906`, -1.3862943611198906`, \ -1.3862943611198906`, -1.3862943611198906`, -1.3862943611198906`, \ -1.3862943611198906`}, "Version" -> {12., 0}, "ID" -> 8955693765171360050, "Output" -> <| "f2" -> <| "Type" -> "NumericalVector", "Weight" -> 1|>|>|>]}, "Invertibility" -> "Impossible", "Missing" -> "Forbidden"|>]}, "Invertibility" -> "Impossible", "Missing" -> "Forbidden"|>], MachineLearning`MLProcessor[ "MergeVectors", <| "Invertibility" -> "Perfect", "Missing" -> "Allowed", "Input" -> <| "f1" -> <|"Type" -> "NumericalVector", "Weight" -> 1|>, "f2" -> <|"Type" -> "NumericalVector", "Weight" -> 1|>|>, "Spans" -> { Span[1, 8], Span[9, 16]}, "Wrappers" -> {SparseArray, SparseArray}, "Output" -> <| "(f1f2)" -> <| "Weight" -> {0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125}, "Type" -> "NumericalVector"|>|>, "Version" -> {12., 0}, "ID" -> 4747858513896072514|>]}, "Invertibility" -> "Impossible", "Missing" -> "Imputed"|>]|>, "Output" -> <| "Preprocessor" -> MachineLearning`MLProcessor[ "ToMLDataset", <| "Input" -> <|"f1" -> <|"Type" -> "Nominal"|>|>, "Output" -> <|"f1" -> <|"Type" -> "Nominal", "Weight" -> 1|>|>, "Preprocessor" -> MachineLearning`MLProcessor["Sequence", <|"Processors" -> { MachineLearning`MLProcessor["List"], MachineLearning`MLProcessor[ "WrapMLDataset", <| "FeatureTypes" -> {"Nominal"}, "FeatureKeys" -> {"f1"}, "FeatureWeights" -> Automatic, "ExampleWeights" -> Automatic, "RawExample" -> Missing["KeyAbsent", "RawExample"]|>]}|>], "ScalarFeature" -> True, "Invertibility" -> "Perfect", "Missing" -> "Allowed"|>], "Processor" -> MachineLearning`MLProcessor[ "Sequence", <| "Input" -> <|"f1" -> <|"Type" -> "Nominal", "Weight" -> 1|>|>, "Output" -> <|"f1" -> <|"Type" -> "Nominal", "Weight" -> 1|>|>, "Processors" -> { MachineLearning`MLProcessor[ "ToVector", <| "Invertibility" -> "Perfect", "Missing" -> "Allowed", "Input" -> <|"f1" -> <|"Type" -> "Nominal", "Weight" -> 1|>|>, "Output" -> <| "f1" -> <|"Type" -> "NominalVector", "Weight" -> 1|>|>, "Version" -> {12., 0}, "ID" -> 6914948501859350705|>], MachineLearning`MLProcessor[ "IntegerEncodeNominalVector", <| "Invertibility" -> "Perfect", "Missing" -> "Allowed", "Input" -> <| "f1" -> <|"Type" -> "NominalVector", "Weight" -> 1|>|>, "Index" -> {<|"output1" -> 1, "output2" -> 2|>}, "MissingCode" -> 0, "Version" -> {12., 0}, "ID" -> 1356389900234704965, "Output" -> <| "f1" -> <|"Type" -> "NominalVector", "Weight" -> 1|>|>|>], MachineLearning`MLProcessor[ "FromVector", <| "Invertibility" -> "Perfect", "Missing" -> "Allowed", "Input" -> <| "f1" -> <| "Type" -> "NominalVector", "Weight" -> 1, "SetSize" -> {2}|>|>, "Output" -> <|"f1" -> <|"Type" -> "Nominal", "Weight" -> 1|>|>, "Version" -> {12., 0}, "ID" -> 6284667471774777187|>], MachineLearning`MLProcessor[ "FirstValues", <| "Info" -> <|"Type" -> "Nominal", "Weight" -> 1, "SetSize" -> 2|>, "Key" -> "f1", "Invertibility" -> "Perfect", "Missing" -> "Allowed"|>]}, "Invertibility" -> "Perfect", "Missing" -> "Allowed"|>], "ProbabilityPostprocessor" -> Identity, "Name" -> "class", "Marginal" -> <|"output1" -> 0.5, "output2" -> 0.5|>|>, "Prior" -> Automatic, "Utility" -> SparseArray[ Automatic, {2, 3}, 0., {1, {{0, 1, 2}, {{2}, {3}}}, {1., 1.}}], "Threshold" -> 0, "TieBreaker" -> RandomChoice, "PerformanceGoal" -> Automatic, "BatchProcessing" -> Automatic, "Model" -> <| "LogProbabilitiesFunction" -> LinearLayer[<| "Type" -> "Linear", "Arrays" -> <| "Weights" -> RawArray["Real32",{{-13.717463493347168`, 14.517847061157227`, 0.03268508240580559, -0.6527844071388245, -0.07077860832214355, \ -0.29379794001579285`, 0.08476421236991882, 0.40708044171333313`, -14.919875144958496`, 14.141423225402832`, -0.03470562398433685, -0.04779186472296715, 0.29993322491645813`, -0.46852850914001465`, 0.07158926129341125, -0.06703520566225052}}], "Biases" -> RawArray["Real32",{-0.10486844927072525`}]|>, "Parameters" -> <| "OutputDimensions" -> {1}, "$OutputSize" -> 1, "$InputSize" -> 16, "$InputDimensions" -> {16}|>, "Inputs" -> <| "Input" -> NeuralNetworks`TensorT[{16}, NeuralNetworks`RealT]|>, "Outputs" -> <| "Output" -> NeuralNetworks`TensorT[{1}, NeuralNetworks`RealT]|>|>, <| "Version" -> "12.0.5", "Unstable" -> False|>], "Processor" -> MachineLearning`MLProcessor[ "FirstValues", <| "Info" -> <|"Weight" -> 2., "Type" -> "NumericalVector"|>, "Key" -> "(f1f2)", "Invertibility" -> "Perfect", "Missing" -> "Allowed"|>], "Method" -> "LogisticRegression", "PostProcessor" -> MachineLearning`MLProcessor["Identity"], "Options" -> <| "L1Regularization" -> <|"Value" -> 0, "Options" -> <||>|>, "L2Regularization" -> <|"Value" -> 0.00001, "Options" -> <||>|>, "OptimizationMethod" -> <|"Value" -> "LBFGS", "Options" -> <||>|>, MaxIterations -> <|"Value" -> 30, "Options" -> <||>|>|>|>, "TrainingInformation" -> <| "PanelCell" -> CellObject[136081], "TrainingFunction" -> Classify, "EMIterations" -> Missing["KeyAbsent", "EMIterations"], "ProcessorEntropyShift" -> 0, "PreprocessingTime" -> 0.170764`5.683941312722175, "LossName" -> "MeanCrossEntropy", "BestModelInformation" -> Dataset[<|"MeanCrossEntropy" -> $CellContext`Around[ 0.5199223077025176, 0.16942134196336783`], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.0005043677773704479, "TestSize" -> 2, "ModelMemory" -> 9200., "ModelUtility" -> -0.10221802982047368`, "TrainingSize" -> 2, "TrainingTime" -> 0.029454805839487792`, "TrainingMemory" -> 167346.66666666666`, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.5199222421484208, 0.2395980069544349], $CellContext`Around[0.5199223732566144, 0.2395979121656469]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}, "Configuration" -> { "LogisticRegression", "L1Regularization" -> 0, "L2Regularization" -> 0.00001, "OptimizationMethod" -> Automatic, MaxIterations -> 30}, "FinalTrainingSize" -> 2|>, TypeSystem`Struct[{ "MeanCrossEntropy", "Accuracy", "EvaluationTime", "TestSize", "ModelMemory", "ModelUtility", "TrainingSize", "TrainingTime", "TrainingMemory", "ExperimentCount", "MeanCrossEntropyHistory", "AccuracyHistory", "Configuration", "FinalTrainingSize"}, { TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Atom[Real], TypeSystem`Atom[Real], TypeSystem`Atom[Integer], TypeSystem`Vector[TypeSystem`AnyType, 2], TypeSystem`Vector[TypeSystem`AnyType, 2], TypeSystem`Tuple[{ TypeSystem`Atom[String], TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`AnyType, TypeSystem`AnyType}], TypeSystem`Atom[Integer]}], <|"ID" -> 50856870677916|>], "Configurations" -> Dataset[<|<| "Value" -> "LogisticRegression", "Options" -> <| "L1Regularization" -> <|"Value" -> 0|>, "L2Regularization" -> <|"Value" -> 0.00001|>, "OptimizationMethod" -> <|"Value" -> Automatic|>, MaxIterations -> <|"Value" -> 30|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.5199223077025176, 0.16942134196336783`], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.0005043677773704479, "TestSize" -> 2, "ModelMemory" -> 9200., "ModelUtility" -> -0.10221802982047368`, "TrainingSize" -> 2, "TrainingTime" -> 0.029454805839487792`, "TrainingMemory" -> 167346.66666666666`, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.5199222421484208, 0.2395980069544349], $CellContext`Around[0.5199223732566144, 0.2395979121656469]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.0005043677773704479, "MeanCrossEntropy" -> $CellContext`Around[ 0.5199223077025176, 0.16942134196336783`], "ModelMemory" -> 9200., "TrainingMemory" -> 167346.66666666666`, "TrainingTime" -> 0.030043901956277548`|>, "Index" -> 1|>, <| "Value" -> "NaiveBayes", "Options" -> <|"SmoothingParameter" -> <|"Value" -> 0.2|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.6931471805599453, 0.1170476946566248], "Accuracy" -> $CellContext`Around[0.5, 0.33071891388307395`], "EvaluationTime" -> 0.00185847260746629, "TestSize" -> 2, "ModelMemory" -> 23120., "ModelUtility" -> -0.42227025607513824`, "TrainingSize" -> 2, "TrainingTime" -> 0.00630957344480193, "TrainingMemory" -> 68554.66666666666, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.6931471805599453, 0.16553043722790364`], $CellContext`Around[ 0.6931471805599453, 0.16553043722790364`]}, "AccuracyHistory" -> { $CellContext`Around[0.25, 0.25000000000000006`], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.00185847260746629, "MeanCrossEntropy" -> $CellContext`Around[ 0.6931471805599453, 0.1170476946566248], "ModelMemory" -> 23120., "TrainingMemory" -> 68554.66666666666, "TrainingTime" -> 0.006435764913697969|>, "Index" -> 2|>, <| "Value" -> "DecisionTree", "Options" -> <| "DistributionSmoothing" -> <|"Value" -> 1|>, "FeatureFraction" -> <|"Value" -> 1|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.6931471805599453, 0.1170476946566248], "Accuracy" -> $CellContext`Around[0.5, 0.33071891388307395`], "EvaluationTime" -> 0.0002773272545101879, "TestSize" -> 2, "ModelMemory" -> 5728., "ModelUtility" -> -0.35985931944586746`, "TrainingSize" -> 2, "TrainingTime" -> 0.009446928632017237, "TrainingMemory" -> 464765.3333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.6931471805599453, 0.16553043722790364`], $CellContext`Around[ 0.6931471805599453, 0.16553043722790364`]}, "AccuracyHistory" -> { $CellContext`Around[0.25, 0.25000000000000006`], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.0002773272545101879, "MeanCrossEntropy" -> $CellContext`Around[ 0.6931471805599453, 0.1170476946566248], "ModelMemory" -> 5728., "TrainingMemory" -> 464765.3333333333, "TrainingTime" -> 0.009635867204657582|>, "Index" -> 3|>, <| "Value" -> "NearestNeighbors", "Options" -> <| "NeighborsNumber" -> <|"Value" -> Automatic|>, "DistributionSmoothing" -> <|"Value" -> 0.5|>, "NearestMethod" -> <|"Value" -> Automatic|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.5654407746184476, 0.1478331014478076], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.00037081403570794394`, "TestSize" -> 2, "ModelMemory" -> 6152., "ModelUtility" -> -0.17397589015694293`, "TrainingSize" -> 2, "TrainingTime" -> 0.004395340777571272, "TrainingMemory" -> 74290.66666666666, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.5654407746184476, 0.20906757703516712`], $CellContext`Around[ 0.5654407746184476, 0.20906757703516712`]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.00037081403570794394`, "MeanCrossEntropy" -> $CellContext`Around[ 0.5654407746184476, 0.1478331014478076], "ModelMemory" -> 6152., "TrainingMemory" -> 74290.66666666666, "TrainingTime" -> 0.004483247593122698|>, "Index" -> 4|>, <| "Value" -> "RandomForest", "Options" -> <| "FeatureFraction" -> <|"Value" -> Automatic|>, "LeafSize" -> <|"Value" -> Automatic|>, "TreeNumber" -> <|"Value" -> Automatic|>, "DistributionSmoothing" -> <|"Value" -> 0.5|>, "Implementation" -> <|"Value" -> Automatic|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.7401840219109932, 0.12293787802175186`], "Accuracy" -> $CellContext`Around[0.25, 0.17677669529663692`], "EvaluationTime" -> 0.0029454805839487794`, "TestSize" -> 2, "ModelMemory" -> 84984., "ModelUtility" -> -0.533527391061646, "TrainingSize" -> 2, "TrainingTime" -> 0.026364828375992903`, "TrainingMemory" -> 153786.66666666666`, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.7251055234374163, 0.16858725138139882`], $CellContext`Around[ 0.7552625203845702, 0.17680113430176175`]}, "AccuracyHistory" -> { $CellContext`Around[0.25, 0.25000000000000006`], $CellContext`Around[0.25, 0.25000000000000006`]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.0029454805839487794`, "MeanCrossEntropy" -> $CellContext`Around[ 0.7401840219109932, 0.12293787802175186`], "ModelMemory" -> 84984., "TrainingMemory" -> 153786.66666666666`, "TrainingTime" -> 0.02689212494351276|>, "Index" -> 5|>, <| "Value" -> "LogisticRegression", "Options" -> <| "L1Regularization" -> <|"Value" -> 0|>, "L2Regularization" -> <|"Value" -> 10.|>, "OptimizationMethod" -> <|"Value" -> Automatic|>, MaxIterations -> <|"Value" -> 30|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.6910777701922841, 0.11705684887641254`], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.00025118864315095795`, "TestSize" -> 2, "ModelMemory" -> 9200., "ModelUtility" -> -0.35697008924907936`, "TrainingSize" -> 2, "TrainingTime" -> 0.006854143078948891, "TrainingMemory" -> 66701.33333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.6910429833353604, 0.16554381080191705`], $CellContext`Around[ 0.6911125570492077, 0.16554294108083073`]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.00025118864315095795`, "MeanCrossEntropy" -> $CellContext`Around[ 0.6910777701922841, 0.11705684887641254`], "ModelMemory" -> 9200., "TrainingMemory" -> 66701.33333333333, "TrainingTime" -> 0.0069912259405278685`|>, "Index" -> 6|>, <| "Value" -> "LogisticRegression", "Options" -> <| "L1Regularization" -> <|"Value" -> 0|>, "L2Regularization" -> <|"Value" -> 100000.|>, "OptimizationMethod" -> <|"Value" -> Automatic|>, MaxIterations -> <|"Value" -> 30|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.6931469710129528, 0.11704769465671871`], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.00025118864315095795`, "TestSize" -> 2, "ModelMemory" -> 9200., "ModelUtility" -> -0.35985940506522074`, "TrainingSize" -> 2, "TrainingTime" -> 0.00630957344480193, "TrainingMemory" -> 65186.666666666664`, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.6931469669342213, 0.16553043722804153`], $CellContext`Around[ 0.6931469750916842, 0.16553043722803118`]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.00025118864315095795`, "MeanCrossEntropy" -> $CellContext`Around[ 0.6931469710129528, 0.11704769465671871`], "ModelMemory" -> 9200., "TrainingMemory" -> 65186.666666666664`, "TrainingTime" -> 0.006435764913697969|>, "Index" -> 7|>, <| "Value" -> "LogisticRegression", "Options" -> <| "L1Regularization" -> <|"Value" -> 0|>, "L2Regularization" -> <|"Value" -> 0.01|>, "OptimizationMethod" -> <|"Value" -> Automatic|>, MaxIterations -> <|"Value" -> 30|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.5393383153932374, 0.1597771779114638], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.00025118864315095795`, "TestSize" -> 2, "ModelMemory" -> 9200., "ModelUtility" -> -0.13330581889689608`, "TrainingSize" -> 2, "TrainingTime" -> 0.015848931924611134`, "TrainingMemory" -> 80048., "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.5393172260882254, 0.22597340671333951`], $CellContext`Around[0.5393594046982495, 0.225944696006231]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.00025118864315095795`, "MeanCrossEntropy" -> $CellContext`Around[ 0.5393383153932374, 0.1597771779114638], "ModelMemory" -> 9200., "TrainingMemory" -> 80048., "TrainingTime" -> 0.016165910563103358`|>, "Index" -> 8|>, <| "Value" -> "LogisticRegression", "Options" -> <| "L1Regularization" -> <|"Value" -> 0|>, "L2Regularization" -> <|"Value" -> 0.0001|>, "OptimizationMethod" -> <|"Value" -> Automatic|>, MaxIterations -> <|"Value" -> 30|>|>|> -> <| "Experiments" -> {<| "MeanCrossEntropy" -> $CellContext`Around[ 0.5203289943542231, 0.1692135501851447], "Accuracy" -> $CellContext`Around[0.75, 0.3061862178478973], "EvaluationTime" -> 0.00025118864315095795`, "TestSize" -> 2, "ModelMemory" -> 9200., "ModelUtility" -> -0.10287686593164258`, "TrainingSize" -> 2, "TrainingTime" -> 0.019952623149688785`, "TrainingMemory" -> 80205.33333333333, "ExperimentCount" -> 2, "MeanCrossEntropyHistory" -> { $CellContext`Around[0.5203284989037839, 0.23930445540824058`], $CellContext`Around[ 0.5203294898046622, 0.23930373980957673`]}, "AccuracyHistory" -> { $CellContext`Around[0.75, 0.4330127018922194], $CellContext`Around[0.75, 0.4330127018922194]}|>}, "PredictedPerformances" -> <| "EvaluationTime" -> 0.00025118864315095795`, "MeanCrossEntropy" -> $CellContext`Around[ 0.5203289943542231, 0.1692135501851447], "ModelMemory" -> 9200., "TrainingMemory" -> 80205.33333333333, "TrainingTime" -> 0.02035167561268256|>, "Index" -> 9|>, <| "Value" -> "LogisticRegression", "Options" -> <| "L1Regularization" -> <|"Value" -> 0|>, "L2Regularization" -> <|"Value" -> 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