FeatureImportanceCalcer

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5. def calc(model: TFullModel, fstrType: EFstrType, data: Pool = null, calcType: ECalcTypeShapValues = ECalcTypeShapValues.Regular): Array[Double]

   

   Supported values of fstrType are FeatureImportance, PredictionValuesChange, LossFunctionChange, PredictionDiff

   Supported values of fstrType are FeatureImportance, PredictionValuesChange, LossFunctionChange, PredictionDiff

   data

   if fstrType is PredictionDiff it is required and must contain 2 samples if fstrType is PredictionValuesChange this param is required in case if model was explicitly trained with flag to store no leaf weights. otherwise it can be null

   returns

   array of feature importances (index corresponds to order of features in the model)

6. def calcInteraction(model: TFullModel): Array[FeatureInteractionScore]

   

7. def calcLossFunctionChange(model: TFullModel, data: Pool, calcType: ECalcTypeShapValues): Array[Double]

   

8. def calcPredictionDiff(model: TFullModel, data: Pool): Array[Double]

   

9. def calcPredictionValuesChange(model: TFullModel, data: Pool): Array[Double]

   

10. def calcShapInteractionValues(model: TFullModel, data: Pool, featureIndices: Pair[Int, Int], featureNames: Pair[String, String], preCalcMode: EPreCalcShapValues, calcType: ECalcTypeShapValues, outputColumns: Array[String]): DataFrame

    

11. def calcShapValues(model: TFullModel, data: Pool, preCalcMode: EPreCalcShapValues, calcType: ECalcTypeShapValues, modelOutputType: EExplainableModelOutput, referenceData: Pool, outputColumns: Array[String]): DataFrame

    

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35. final def notify(): Unit

    

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36. final def notifyAll(): Unit

    

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37. def prepareTrees(model: TFullModel, data: Pool, preCalcMode: EPreCalcShapValues, calcInternalValues: Boolean, calcType: ECalcTypeShapValues, calcShapValuesByLeaf: Boolean, localExecutor: TLocalExecutor, modelOutputType: EExplainableModelOutput = EExplainableModelOutput.Raw, referenceData: Pool = null): TShapPreparedTrees

    

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