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  • package root
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    root
  • package ai
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    root
  • package catboost
    Definition Classes
    ai
  • package spark

    CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.

    CatBoost is a machine learning algorithm that uses gradient boosting on decision trees.

    Overview

    This package provides classes that implement interfaces from Apache Spark Machine Learning Library (MLLib).

    For binary and multi- classification problems use CatBoostClassifier, for regression use CatBoostRegressor.

    These classes implement usual fit method of org.apache.spark.ml.Predictor that accept a single org.apache.spark.sql.DataFrame for training, but you can also use other fit method that accepts additional datasets for computing evaluation metrics and overfitting detection similarily to CatBoost's other APIs.

    This package also contains Pool class that is CatBoost's abstraction of a dataset. It contains additional information compared to simple org.apache.spark.sql.DataFrame.

    It is also possible to create Pool with quantized features before training by calling quantize method. This is useful if this dataset is used for training multiple times and quantization parameters do not change. Pre-quantized Pool allows to cache quantized features data and so do not re-run feature quantization step at the start of an each training.

    Detailed documentation is available on https://catboost.ai/docs/

    Definition Classes
    catboost
  • package impl
    Definition Classes
    spark
  • package pyspark_wrapper_generator
    Definition Classes
    impl
  • CtrFeatures
  • CtrsContext
  • FeatureImportanceCalcer
c

ai.catboost.spark.impl

FeatureImportanceCalcer

class FeatureImportanceCalcer extends Logging

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Instance Constructors

  1. new FeatureImportanceCalcer()

Value Members

  1. final def !=(arg0: Any): Boolean
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  2. final def ##(): Int
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  4. final def asInstanceOf[T0]: T0
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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
  12. def clone(): AnyRef
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  18. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
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  19. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
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  20. final def isInstanceOf[T0]: Boolean
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  21. def isTraceEnabled(): Boolean
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  22. def log: Logger
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  23. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
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  24. def logDebug(msg: ⇒ String): Unit
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  25. def logError(msg: ⇒ String, throwable: Throwable): Unit
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  26. def logError(msg: ⇒ String): Unit
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  27. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
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  28. def logInfo(msg: ⇒ String): Unit
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  29. def logName: String
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  30. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
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  31. def logTrace(msg: ⇒ String): Unit
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  32. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
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  33. def logWarning(msg: ⇒ String): Unit
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  34. final def ne(arg0: AnyRef): Boolean
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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
  38. final def synchronized[T0](arg0: ⇒ T0): T0
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  39. def toString(): String
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  40. final def wait(): Unit
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  41. final def wait(arg0: Long, arg1: Int): Unit
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  42. final def wait(arg0: Long): Unit
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