Class

com.salesforce.op.stages.impl.regression

OpXGBoostRegressor

Related Doc: package regression

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class OpXGBoostRegressor extends OpPredictorWrapper[XGBoostRegressor, XGBoostRegressionModel] with OpXGBoostRegressorParams

Wrapper around XGBoost regressor XGBoostRegressor

Linear Supertypes
OpXGBoostRegressorParams, OpXGBoostGeneralParamsDefaults, XGBoostRegressorParams, NonParamVariables, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasGroupCol, HasWeightCol, HasBaseMarginCol, LearningTaskParams, BoosterParams, GeneralParams, OpPredictorWrapper[XGBoostRegressor, XGBoostRegressionModel], SparkWrapperParams[XGBoostRegressor], OpPipelineStage2[RealNN, OPVector, Prediction], HasOut[Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[XGBoostRegressionModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpXGBoostRegressor
  2. OpXGBoostRegressorParams
  3. OpXGBoostGeneralParamsDefaults
  4. XGBoostRegressorParams
  5. NonParamVariables
  6. HasContribPredictionCol
  7. HasLeafPredictionCol
  8. ParamMapFuncs
  9. HasGroupCol
  10. HasWeightCol
  11. HasBaseMarginCol
  12. LearningTaskParams
  13. BoosterParams
  14. GeneralParams
  15. OpPredictorWrapper
  16. SparkWrapperParams
  17. OpPipelineStage2
  18. HasOut
  19. HasIn2
  20. HasIn1
  21. OpPipelineStage
  22. OpPipelineStageBase
  23. MLWritable
  24. OpPipelineStageParams
  25. InputParams
  26. Estimator
  27. PipelineStage
  28. Logging
  29. Params
  30. Serializable
  31. Serializable
  32. Identifiable
  33. AnyRef
  34. Any
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Visibility
  1. Public
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Instance Constructors

  1. new OpXGBoostRegressor(uid: String = UID[OpXGBoostRegressor])

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Type Members

  1. final type InputFeatures = (FeatureLike[RealNN], FeatureLike[OPVector])

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    Input Features type

    Input Features type

    Definition Classes
    OpPipelineStage2OpPipelineStageInputParams
  2. final type OutputFeatures = FeatureLike[Prediction]

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    Definition Classes
    OpPipelineStageOpPipelineStageBase

Value Members

  1. final def !=(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  2. final def ##(): Int

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    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T

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    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  5. def MLlib2XGBoostParams: Map[String, Any]

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    Definition Classes
    ParamMapFuncs
  6. def XGBoostToMLlibParams(xgboostParams: Map[String, Any]): Unit

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    Definition Classes
    ParamMapFuncs
  7. final val alpha: DoubleParam

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    Definition Classes
    BoosterParams
  8. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  9. final val baseMarginCol: Param[String]

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    Definition Classes
    HasBaseMarginCol
  10. final val baseScore: DoubleParam

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    Definition Classes
    LearningTaskParams
  11. final val cacheTrainingSet: BooleanParam

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    Definition Classes
    LearningTaskParams
  12. final def checkInputLength(features: Array[_]): Boolean

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    Checks the input length

    Checks the input length

    features

    input features

    returns

    true is input size as expected, false otherwise

    Definition Classes
    OpPipelineStage2InputParams
  13. def checkSerializable: Try[Unit]

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    Check if the stage is serializable

    Check if the stage is serializable

    returns

    Failure if not serializable

    Definition Classes
    OpPipelineStageBase
  14. final val checkpointInterval: IntParam

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    Definition Classes
    GeneralParams
  15. final val checkpointPath: Param[String]

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    Definition Classes
    GeneralParams
  16. final def clear(param: Param[_]): OpXGBoostRegressor.this.type

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    Definition Classes
    Params
  17. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  18. final val colsampleBylevel: DoubleParam

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    Definition Classes
    BoosterParams
  19. final val colsampleBytree: DoubleParam

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    Definition Classes
    BoosterParams
  20. final val contribPredictionCol: Param[String]

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    Definition Classes
    HasContribPredictionCol
  21. final def copy(extra: ParamMap): OpXGBoostRegressor.this.type

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    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    This method is used to make a copy of the instance with new parameters in several methods in spark internals Default will find the constructor and make a copy for any class (AS LONG AS ALL CONSTRUCTOR PARAMS ARE VALS, this is why type tags are written as implicit vals in base classes).

    Note: that the convention in spark is to have the uid be a constructor argument, so that copies will share a uid with the original (developers should follow this convention).

    extra

    new parameters want to add to instance

    returns

    a new instance with the same uid

    Definition Classes
    OpPipelineStageBase → Params
  22. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  23. final val customEval: CustomEvalParam

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    Definition Classes
    GeneralParams
  24. final val customObj: CustomObjParam

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    Definition Classes
    GeneralParams
  25. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  26. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  27. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any
  28. final val eta: DoubleParam

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    Definition Classes
    BoosterParams
  29. final val evalMetric: Param[String]

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    Definition Classes
    LearningTaskParams
  30. var evalSetsMap: Map[String, DataFrame]

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    Attributes
    protected
    Definition Classes
    NonParamVariables
  31. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  32. def explainParams(): String

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    Definition Classes
    Params
  33. final def extractParamMap(): ParamMap

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    Definition Classes
    Params
  34. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params
  35. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  36. def fit(dataset: Dataset[_]): OpPredictorWrapperModel[XGBoostRegressionModel]

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    Function that fits the binary model

    Function that fits the binary model

    Definition Classes
    OpPredictorWrapper → Estimator
  37. def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[OpPredictorWrapperModel[XGBoostRegressionModel]]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  38. def fit(dataset: Dataset[_], paramMap: ParamMap): OpPredictorWrapperModel[XGBoostRegressionModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  39. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): OpPredictorWrapperModel[XGBoostRegressionModel]

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  40. final val gamma: DoubleParam

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    Definition Classes
    BoosterParams
  41. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  42. final def getAlpha: Double

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    Definition Classes
    BoosterParams
  43. final def getBaseMarginCol: String

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    Definition Classes
    HasBaseMarginCol
  44. final def getBaseScore: Double

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    Definition Classes
    LearningTaskParams
  45. final def getCheckpointInterval: Int

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    Definition Classes
    GeneralParams
  46. final def getCheckpointPath: String

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    Definition Classes
    GeneralParams
  47. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  48. final def getColsampleBylevel: Double

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    Definition Classes
    BoosterParams
  49. final def getColsampleBytree: Double

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    Definition Classes
    BoosterParams
  50. final def getContribPredictionCol: String

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    Definition Classes
    HasContribPredictionCol
  51. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  52. final def getEta: Double

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    Definition Classes
    BoosterParams
  53. final def getEvalMetric: String

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    Definition Classes
    LearningTaskParams
  54. def getEvalSets(params: Map[String, Any]): Map[String, DataFrame]

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    Definition Classes
    NonParamVariables
  55. final def getGamma: Double

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    Definition Classes
    BoosterParams
  56. final def getGroupCol: String

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    Definition Classes
    HasGroupCol
  57. final def getGrowPolicy: String

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    Definition Classes
    BoosterParams
  58. def getInputColParamNames(): Array[String]

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    Gets names of parameters that control input columns for Spark stage

    Gets names of parameters that control input columns for Spark stage

    Definition Classes
    SparkWrapperParams
  59. final def getInputFeature[T <: FeatureType](i: Int): Option[FeatureLike[T]]

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    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    Gets an input feature Note: this method IS NOT safe to use outside the driver, please use getTransientFeature method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  60. final def getInputFeatures(): Array[OPFeature]

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    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    Gets the input features Note: this method IS NOT safe to use outside the driver, please use getTransientFeatures method instead

    returns

    array of features

    Definition Classes
    InputParams
    Exceptions thrown

    NoSuchElementException if the features are not set

    RuntimeException in case one of the features is null

  61. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  62. final def getInteractionConstraints: String

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    Definition Classes
    BoosterParams
  63. final def getLambda: Double

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    Definition Classes
    BoosterParams
  64. final def getLambdaBias: Double

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    Definition Classes
    BoosterParams
  65. final def getLeafPredictionCol: String

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    Definition Classes
    HasLeafPredictionCol
  66. final def getMaxBins: Int

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    Definition Classes
    BoosterParams
  67. final def getMaxDeltaStep: Double

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    Definition Classes
    BoosterParams
  68. final def getMaxDepth: Int

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    Definition Classes
    BoosterParams
  69. final def getMaxLeaves: Int

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    Definition Classes
    BoosterParams
  70. final def getMaximizeEvaluationMetrics: Boolean

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    Definition Classes
    LearningTaskParams
  71. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  72. final def getMinChildWeight: Double

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    Definition Classes
    BoosterParams
  73. final def getMissing: Float

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    Definition Classes
    GeneralParams
  74. final def getMonotoneConstraints: String

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    Definition Classes
    BoosterParams
  75. final def getNormalizeType: String

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    Definition Classes
    BoosterParams
  76. final def getNthread: Int

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    Definition Classes
    GeneralParams
  77. final def getNumEarlyStoppingRounds: Int

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    Definition Classes
    LearningTaskParams
  78. final def getNumRound: Int

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    Definition Classes
    GeneralParams
  79. final def getNumWorkers: Int

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    Definition Classes
    GeneralParams
  80. final def getObjective: String

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    Definition Classes
    LearningTaskParams
  81. final def getObjectiveType: String

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    Definition Classes
    LearningTaskParams
  82. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  83. def getOutput(): FeatureLike[Prediction]

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    Output features that will be created by this stage

    Output features that will be created by this stage

    returns

    feature of type OutputFeatures

    Definition Classes
    HasOut → OpPipelineStageBase
  84. def getOutputColParamNames(): Array[String]

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    Gets names of parameters that control output columns for Spark stage

    Gets names of parameters that control output columns for Spark stage

    Definition Classes
    SparkWrapperParams
  85. final def getOutputFeatureName: String

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    Name of output feature (i.e.

    Name of output feature (i.e. column created by this stage)

    Definition Classes
    OpPipelineStage
  86. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  87. final def getRateDrop: Double

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    Definition Classes
    BoosterParams
  88. final def getSampleType: String

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    Definition Classes
    BoosterParams
  89. final def getScalePosWeight: Double

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    Definition Classes
    BoosterParams
  90. final def getSeed: Long

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    Definition Classes
    GeneralParams
  91. final def getSilent: Int

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    Definition Classes
    GeneralParams
  92. final def getSketchEps: Double

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    Definition Classes
    BoosterParams
  93. final def getSkipDrop: Double

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    Definition Classes
    BoosterParams
  94. def getSparkMlStage(): Option[XGBoostRegressor]

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    Method to access the spark stage being wrapped

    Method to access the spark stage being wrapped

    returns

    Option of spark ml stage

    Definition Classes
    SparkWrapperParams
  95. def getStageSavePath(): Option[String]

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    Gets a save path for wrapped spark stage

    Gets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  96. final def getSubsample: Double

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    Definition Classes
    BoosterParams
  97. final def getTimeoutRequestWorkers: Long

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    Definition Classes
    GeneralParams
  98. final def getTrainTestRatio: Double

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    Definition Classes
    LearningTaskParams
  99. final def getTransientFeature(i: Int): Option[TransientFeature]

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    Gets an input feature at index i

    Gets an input feature at index i

    i

    input index

    returns

    maybe an input feature

    Definition Classes
    InputParams
  100. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  101. final def getTreeLimit: Int

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    Definition Classes
    BoosterParams
  102. final def getTreeMethod: String

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    Definition Classes
    BoosterParams
  103. final def getUseExternalMemory: Boolean

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    Definition Classes
    GeneralParams
  104. final def getVerbosity: Int

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    Definition Classes
    GeneralParams
  105. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  106. final val groupCol: Param[String]

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    Definition Classes
    HasGroupCol
  107. final val growPolicy: Param[String]

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    Definition Classes
    BoosterParams
  108. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params
  109. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params
  110. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  111. final def in1: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn1
  112. final def in2: TransientFeature

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    Attributes
    protected
    Definition Classes
    HasIn2
  113. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  114. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  115. final def inputAsArray(in: InputFeatures): Array[OPFeature]

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    Function to convert InputFeatures to an Array of FeatureLike

    Function to convert InputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStage2InputParams
  116. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  117. val inputParam2Name: String

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    Definition Classes
    OpPredictorWrapper
  118. final val interactionConstraints: Param[String]

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    Definition Classes
    BoosterParams
  119. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params
  120. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any
  121. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params
  122. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  123. final val lambda: DoubleParam

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    Definition Classes
    BoosterParams
  124. final val lambdaBias: DoubleParam

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    Definition Classes
    BoosterParams
  125. final val leafPredictionCol: Param[String]

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    Definition Classes
    HasLeafPredictionCol
  126. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging
  127. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  128. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  129. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  130. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  131. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  132. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  133. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging
  134. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  135. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  136. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  137. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging
  138. final val maxBins: IntParam

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    Definition Classes
    BoosterParams
  139. final val maxDeltaStep: DoubleParam

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    Definition Classes
    BoosterParams
  140. final val maxDepth: IntParam

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    Definition Classes
    BoosterParams
  141. final val maxLeaves: IntParam

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    Definition Classes
    BoosterParams
  142. final val maximizeEvaluationMetrics: BooleanParam

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    Definition Classes
    LearningTaskParams
  143. final val minChildWeight: DoubleParam

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    Definition Classes
    BoosterParams
  144. final val missing: FloatParam

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    Definition Classes
    GeneralParams
  145. final val monotoneConstraints: Param[String]

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    Definition Classes
    BoosterParams
  146. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  147. final val normalizeType: Param[String]

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    Definition Classes
    BoosterParams
  148. final def notify(): Unit

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    Definition Classes
    AnyRef
  149. final def notifyAll(): Unit

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    Definition Classes
    AnyRef
  150. final val nthread: IntParam

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    Definition Classes
    GeneralParams
  151. final val numEarlyStoppingRounds: IntParam

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    Definition Classes
    LearningTaskParams
  152. final val numRound: IntParam

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    Definition Classes
    GeneralParams
  153. final val numWorkers: IntParam

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    Definition Classes
    GeneralParams
  154. final val objective: Param[String]

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    Definition Classes
    LearningTaskParams
  155. final val objectiveType: Param[String]

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    Definition Classes
    LearningTaskParams
  156. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

    Attributes
    protected
    Definition Classes
    OpPipelineStageParams
  157. def onSetInput(): Unit

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpXGBoostRegressorInputParams
  158. val operationName: String

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    Short unique name of the operation this stage performs

    Short unique name of the operation this stage performs

    returns

    operation name

    Definition Classes
    OpPredictorWrapperOpPipelineStageBase
  159. final def outputAsArray(out: OutputFeatures): Array[OPFeature]

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    Function to convert OutputFeatures to an Array of FeatureLike

    Function to convert OutputFeatures to an Array of FeatureLike

    returns

    an Array of FeatureLike

    Definition Classes
    OpPipelineStageOpPipelineStageBase
  160. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  161. def outputIsResponse: Boolean

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    Should output feature be a response? Yes, if any of the input features are.

    Should output feature be a response? Yes, if any of the input features are.

    returns

    true if the the output feature should be a response

    Definition Classes
    OpPipelineStage
  162. val outputParamName: String

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    Definition Classes
    OpPredictorWrapper
  163. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  164. val predictor: XGBoostRegressor

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  165. final val rateDrop: DoubleParam

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    Definition Classes
    BoosterParams
  166. final val sampleType: Param[String]

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    Definition Classes
    BoosterParams
  167. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  168. final val scalePosWeight: DoubleParam

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    Definition Classes
    BoosterParams
  169. final val seed: LongParam

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    Definition Classes
    GeneralParams
  170. final def set(paramPair: ParamPair[_]): OpXGBoostRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params
  171. final def set(param: String, value: Any): OpXGBoostRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params
  172. final def set[T](param: Param[T], value: T): OpXGBoostRegressor.this.type

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    Definition Classes
    Params
  173. def setAlpha(value: Double): OpXGBoostRegressor.this.type

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    L1 regularization term on weights, increase this value will make model more conservative.

    L1 regularization term on weights, increase this value will make model more conservative. [default=0]

  174. def setBaseMarginCol(value: String): OpXGBoostRegressor.this.type

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    Initial prediction (aka base margin) column name.

  175. def setBaseScore(value: Double): OpXGBoostRegressor.this.type

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    Specify the learning task and the corresponding learning objective.

    Specify the learning task and the corresponding learning objective. options: reg:linear, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:linear

  176. def setCheckpointInterval(value: Int): OpXGBoostRegressor.this.type

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    Checkpoint interval (>= 1) or disable checkpoint (-1).

    Checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the trained model will get checkpointed every 10 iterations. Note: checkpoint_path must also be set if the checkpoint interval is greater than 0.

  177. def setCheckpointPath(value: String): OpXGBoostRegressor.this.type

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    The hdfs folder to load and save checkpoint boosters.

    The hdfs folder to load and save checkpoint boosters. default: empty_string

  178. def setColsampleBylevel(value: Double): OpXGBoostRegressor.this.type

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    Subsample ratio of columns for each split, in each level.

    Subsample ratio of columns for each split, in each level. [default=1] range: (0,1]

  179. def setColsampleBytree(value: Double): OpXGBoostRegressor.this.type

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    Subsample ratio of columns when constructing each tree.

    Subsample ratio of columns when constructing each tree. [default=1] range: (0,1]

  180. def setCustomEval(value: EvalTrait): OpXGBoostRegressor.this.type

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    Customized evaluation function provided by user.

    Customized evaluation function provided by user. default: null

  181. def setCustomObj(value: ObjectiveTrait): OpXGBoostRegressor.this.type

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    Customized objective function provided by user.

    Customized objective function provided by user. default: null

  182. final def setDefault(paramPairs: ParamPair[_]*): OpXGBoostRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params
  183. final def setDefault[T](param: Param[T], value: T): OpXGBoostRegressor.this.type

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    Attributes
    protected
    Definition Classes
    Params
  184. def setEta(value: Double): OpXGBoostRegressor.this.type

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    Step size shrinkage used in update to prevents overfitting.

    Step size shrinkage used in update to prevents overfitting. After each boosting step, we can directly get the weights of new features and eta actually shrinks the feature weights to make the boosting process more conservative. [default=0.3] range: [0,1]

  185. def setEvalMetric(value: String): OpXGBoostRegressor.this.type

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    Evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking).

    Evaluation metrics for validation data, a default metric will be assigned according to objective(rmse for regression, and error for classification, mean average precision for ranking). options: rmse, mae, logloss, error, merror, mlogloss, auc, aucpr, ndcg, map, gamma-deviance

  186. def setEvalSets(evalSets: Map[String, DataFrame]): OpXGBoostRegressor.this.type

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    Definition Classes
    NonParamVariables
  187. def setGamma(value: Double): OpXGBoostRegressor.this.type

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    Minimum loss reduction required to make a further partition on a leaf node of the tree.

    Minimum loss reduction required to make a further partition on a leaf node of the tree. the larger, the more conservative the algorithm will be. [default=0] range: [0, Double.MaxValue]

  188. def setGroupCol(value: String): OpXGBoostRegressor.this.type

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    Group column name

  189. def setGrowPolicy(value: String): OpXGBoostRegressor.this.type

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    Growth policy for fast histogram algorithm

  190. final def setInput(features: InputFeatures): OpXGBoostRegressor.this.type

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    Input features that will be used by the stage

    Input features that will be used by the stage

    returns

    feature of type InputFeatures

    Definition Classes
    OpPipelineStageBase
  191. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpXGBoostRegressor.this.type

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    Sets input features

    Sets input features

    S

    feature like type

    features

    array of input features

    returns

    this stage

    Attributes
    protected
    Definition Classes
    InputParams
  192. def setLambda(value: Double): OpXGBoostRegressor.this.type

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    L2 regularization term on weights, increase this value will make model more conservative.

    L2 regularization term on weights, increase this value will make model more conservative. [default=1]

  193. def setLambdaBias(value: Double): OpXGBoostRegressor.this.type

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    Parameter of linear booster L2 regularization term on bias, default 0(no L1 reg on bias because it is not important)

  194. def setMaxBins(value: Int): OpXGBoostRegressor.this.type

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    Maximum number of bins in histogram

  195. def setMaxDeltaStep(value: Double): OpXGBoostRegressor.this.type

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    Maximum delta step we allow each tree's weight estimation to be.

    Maximum delta step we allow each tree's weight estimation to be. If the value is set to 0, it means there is no constraint. If it is set to a positive value, it can help making the update step more conservative. Usually this parameter is not needed, but it might help in logistic regression when class is extremely imbalanced. Set it to value of 1-10 might help control the update. [default=0] range: [0, Double.MaxValue]

  196. def setMaxDepth(value: Int): OpXGBoostRegressor.this.type

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    Maximum depth of a tree, increase this value will make model more complex / likely to be overfitting.

    Maximum depth of a tree, increase this value will make model more complex / likely to be overfitting. [default=6] range: [1, Int.MaxValue]

  197. def setMaxLeaves(value: Int): OpXGBoostRegressor.this.type

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    Maximum number of nodes to be added.

    Maximum number of nodes to be added. Only relevant when grow_policy=lossguide is set.

  198. def setMaximizeEvaluationMetrics(value: Boolean): OpXGBoostRegressor.this.type

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    Define the expected optimization to the evaluation metrics, true to maximize otherwise minimize it

  199. final def setMetadata(m: Metadata): OpXGBoostRegressor.this.type

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    Definition Classes
    OpPipelineStageParams
  200. def setMinChildWeight(value: Double): OpXGBoostRegressor.this.type

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    Minimum sum of instance weight(hessian) needed in a child.

    Minimum sum of instance weight(hessian) needed in a child. If the tree partition step results in a leaf node with the sum of instance weight less than min_child_weight, then the building process will give up further partitioning. In linear regression mode, this simply corresponds to minimum number of instances needed to be in each node. The larger, the more conservative the algorithm will be. [default=1] range: [0, Double.MaxValue]

  201. def setMissing(value: Float): OpXGBoostRegressor.this.type

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    The value treated as missing

  202. def setNormalizeType(value: String): OpXGBoostRegressor.this.type

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    Parameter of Dart booster.

    Parameter of Dart booster. type of normalization algorithm, options: {'tree', 'forest'}. [default="tree"]

  203. def setNthread(value: Int): OpXGBoostRegressor.this.type

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    Number of threads used by per worker.

    Number of threads used by per worker. default 1

  204. def setNumEarlyStoppingRounds(value: Int): OpXGBoostRegressor.this.type

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    If non-zero, the training will be stopped after a specified number of consecutive increases in any evaluation metric.

  205. def setNumRound(value: Int): OpXGBoostRegressor.this.type

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    The number of rounds for boosting

  206. def setNumWorkers(value: Int): OpXGBoostRegressor.this.type

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    Number of workers used to train xgboost model.

    Number of workers used to train xgboost model. default: 1

  207. def setObjective(value: String): OpXGBoostRegressor.this.type

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    Specify the learning task and the corresponding learning objective.

    Specify the learning task and the corresponding learning objective. options: reg:squarederror, reg:logistic, binary:logistic, binary:logitraw, count:poisson, multi:softmax, multi:softprob, rank:pairwise, reg:gamma. default: reg:squarederror

  208. def setObjectiveType(value: String): OpXGBoostRegressor.this.type

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    Objective type used for training.

    Objective type used for training. For options see ml.dmlc.xgboost4j.scala.spark.params.LearningTaskParams

  209. def setOutputFeatureName(name: String): OpXGBoostRegressor.this.type

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    Definition Classes
    OpPipelineStage
  210. def setRateDrop(value: Double): OpXGBoostRegressor.this.type

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    Parameter of Dart booster.

    Parameter of Dart booster. dropout rate. [default=0.0] range: [0.0, 1.0]

  211. def setSampleType(value: String): OpXGBoostRegressor.this.type

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    Parameter for Dart booster.

    Parameter for Dart booster. Type of sampling algorithm. "uniform": dropped trees are selected uniformly. "weighted": dropped trees are selected in proportion to weight. [default="uniform"]

  212. def setScalePosWeight(value: Double): OpXGBoostRegressor.this.type

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    Control the balance of positive and negative weights, useful for unbalanced classes.

    Control the balance of positive and negative weights, useful for unbalanced classes. A typical value to consider: sum(negative cases) / sum(positive cases). [default=1]

  213. def setSeed(value: Long): OpXGBoostRegressor.this.type

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    Random seed for the C++ part of XGBoost and train/test splitting.

  214. def setSilent(value: Int): OpXGBoostRegressor.this.type

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    0 means printing running messages, 1 means silent mode.

    0 means printing running messages, 1 means silent mode. default: 0

  215. def setSketchEps(value: Double): OpXGBoostRegressor.this.type

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    This is only used for approximate greedy algorithm.

    This is only used for approximate greedy algorithm. This roughly translated into O(1 / sketch_eps) number of bins. Compared to directly select number of bins, this comes with theoretical guarantee with sketch accuracy. [default=0.03] range: (0, 1)

  216. def setSkipDrop(value: Double): OpXGBoostRegressor.this.type

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    Parameter of Dart booster.

    Parameter of Dart booster. probability of skip dropout. If a dropout is skipped, new trees are added in the same manner as gbtree. [default=0.0] range: [0.0, 1.0]

  217. def setSparkMlStage(stage: Option[XGBoostRegressor]): OpXGBoostRegressor.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  218. def setStageSavePath(path: String): OpXGBoostRegressor.this.type

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    Sets a save path for wrapped spark stage

    Sets a save path for wrapped spark stage

    Definition Classes
    SparkWrapperParams
  219. def setSubsample(value: Double): OpXGBoostRegressor.this.type

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    Subsample ratio of the training instance.

    Subsample ratio of the training instance. Setting it to 0.5 means that XGBoost randomly collected half of the data instances to grow trees and this will prevent overfitting. [default=1] range:(0,1]

  220. def setTimeoutRequestWorkers(value: Long): OpXGBoostRegressor.this.type

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    The maximum time to wait for the job requesting new workers.

    The maximum time to wait for the job requesting new workers. default: 30 minutes

  221. def setTrackerConf(value: TrackerConf): OpXGBoostRegressor.this.type

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    Rabit tracker configurations.

    Rabit tracker configurations. The parameter must be provided as an instance of the TrackerConf class, which has the following definition:

    case class TrackerConf(workerConnectionTimeout: Duration, trainingTimeout: Duration, trackerImpl: String)

    See below for detailed explanations.

    • trackerImpl: Select the implementation of Rabit tracker. default: "python"

    Choice between "python" or "scala". The former utilizes the Java wrapper of the Python Rabit tracker (in dmlc_core), and does not support timeout settings. The "scala" version removes Python components, and fully supports timeout settings.

    • workerConnectionTimeout: the maximum wait time for all workers to connect to the tracker. default: 0 millisecond (no timeout)

    The timeout value should take the time of data loading and pre-processing into account, due to the lazy execution of Spark's operations. Alternatively, you may force Spark to perform data transformation before calling XGBoost.train(), so that this timeout truly reflects the connection delay. Set a reasonable timeout value to prevent model training/testing from hanging indefinitely, possible due to network issues. Note that zero timeout value means to wait indefinitely (equivalent to Duration.Inf). Ignored if the tracker implementation is "python".

  222. def setTrainTestRatio(value: Double): OpXGBoostRegressor.this.type

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    Fraction of training points to use for testing.

  223. def setTreeMethod(value: String): OpXGBoostRegressor.this.type

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    The tree construction algorithm used in XGBoost.

    The tree construction algorithm used in XGBoost. options: {'auto', 'exact', 'approx'} [default='auto']

  224. def setUseExternalMemory(value: Boolean): OpXGBoostRegressor.this.type

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    Whether to use external memory as cache.

    Whether to use external memory as cache. default: false

  225. def setWeightCol(value: String): OpXGBoostRegressor.this.type

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    Weight column name.

    Weight column name. If this is not set or empty, we treat all instance weights as 1.0.

  226. final val silent: IntParam

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    Definition Classes
    GeneralParams
  227. final val sketchEps: DoubleParam

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    Definition Classes
    BoosterParams
  228. final val skipDrop: DoubleParam

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    Definition Classes
    BoosterParams
  229. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  230. final val sparkMlStage: SparkStageParam[XGBoostRegressor]

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    Definition Classes
    SparkWrapperParams
  231. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  232. final def stageName: String

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    Stage unique name consisting of the stage operation name and uid

    Stage unique name consisting of the stage operation name and uid

    returns

    stage name

    Definition Classes
    OpPipelineStageBase
  233. final val subsample: DoubleParam

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    Definition Classes
    BoosterParams
  234. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  235. final val timeoutRequestWorkers: LongParam

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    Definition Classes
    GeneralParams
  236. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  237. final val trackerConf: TrackerConfParam

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    Definition Classes
    GeneralParams
  238. final val trainTestRatio: DoubleParam

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    Definition Classes
    LearningTaskParams
  239. final def transformSchema(schema: StructType): StructType

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    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    This function translates the input and output features into spark schema checks and changes that will occur on the underlying data frame

    schema

    schema of the input data frame

    returns

    a new schema with the output features added

    Definition Classes
    OpPipelineStageBase
  240. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  241. final val treeLimit: IntParam

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    Definition Classes
    BoosterParams
  242. final val treeMethod: Param[String]

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    Definition Classes
    BoosterParams
  243. implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

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    Definition Classes
    OpPredictorWrapper
  244. implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

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    Definition Classes
    OpPredictorWrapper
  245. implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

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    Type tag of the output

    Type tag of the output

    Definition Classes
    OpPredictorWrapper → HasOut
  246. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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    Type tag of the output value

    Type tag of the output value

    Definition Classes
    OpPredictorWrapper → HasOut
  247. val uid: String

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    stage uid

    stage uid

    Definition Classes
    OpPredictorWrapper → Identifiable
  248. final val useExternalMemory: BooleanParam

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    Definition Classes
    GeneralParams
  249. final val verbosity: IntParam

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    Definition Classes
    GeneralParams
  250. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  251. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  252. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  253. final val weightCol: Param[String]

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    Definition Classes
    HasWeightCol
  254. final def write: MLWriter

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from OpXGBoostRegressorParams

Inherited from XGBoostRegressorParams

Inherited from NonParamVariables

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from ParamMapFuncs

Inherited from HasGroupCol

Inherited from HasWeightCol

Inherited from HasBaseMarginCol

Inherited from LearningTaskParams

Inherited from BoosterParams

Inherited from GeneralParams

Inherited from OpPredictorWrapper[XGBoostRegressor, XGBoostRegressionModel]

Inherited from SparkWrapperParams[XGBoostRegressor]

Inherited from HasOut[Prediction]

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from Estimator[OpPredictorWrapperModel[XGBoostRegressionModel]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Ungrouped