Class

com.salesforce.op.stages.impl.classification

OpXGBoostClassifier

Related Doc: package classification

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class OpXGBoostClassifier extends OpPredictorWrapper[XGBoostClassifier, XGBoostClassificationModel] with OpXGBoostClassifierParams

Wrapper around XGBoost classifier XGBoostClassifier

Linear Supertypes
OpXGBoostClassifierParams, OpXGBoostGeneralParamsDefaults, XGBoostClassifierParams, HasContribPredictionCol, HasLeafPredictionCol, ParamMapFuncs, HasNumClass, HasBaseMarginCol, HasWeightCol, BoosterParams, LearningTaskParams, GeneralParams, OpPredictorWrapper[XGBoostClassifier, XGBoostClassificationModel], SparkWrapperParams[XGBoostClassifier], OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[XGBoostClassificationModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpXGBoostClassifier
  2. OpXGBoostClassifierParams
  3. OpXGBoostGeneralParamsDefaults
  4. XGBoostClassifierParams
  5. HasContribPredictionCol
  6. HasLeafPredictionCol
  7. ParamMapFuncs
  8. HasNumClass
  9. HasBaseMarginCol
  10. HasWeightCol
  11. BoosterParams
  12. LearningTaskParams
  13. GeneralParams
  14. OpPredictorWrapper
  15. SparkWrapperParams
  16. OpPipelineStage2
  17. HasIn2
  18. HasIn1
  19. OpPipelineStage
  20. OpPipelineStageBase
  21. MLWritable
  22. OpPipelineStageParams
  23. InputParams
  24. Estimator
  25. PipelineStage
  26. Logging
  27. Params
  28. Serializable
  29. Serializable
  30. Identifiable
  31. AnyRef
  32. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

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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 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
  12. 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
  13. final val checkpointInterval: IntParam

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

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

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    Definition Classes
    Params
  16. def clone(): AnyRef

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

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    Definition Classes
    BoosterParams
  18. final val colsampleBytree: DoubleParam

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

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    Definition Classes
    HasContribPredictionCol
  20. final def copy(extra: ParamMap): OpXGBoostClassifier.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
  21. def copyValues[T <: Params](to: T, extra: ParamMap): T

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

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    Definition Classes
    GeneralParams
  23. final val customObj: CustomObjParam

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

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

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

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

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

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    Definition Classes
    LearningTaskParams
  29. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  30. def explainParams(): String

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

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

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    Definition Classes
    Params
  33. def finalize(): Unit

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

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    Function that fits the binary model

    Function that fits the binary model

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

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

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

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

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

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    Definition Classes
    Params
  40. final def getAlpha: Double

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    Definition Classes
    BoosterParams
  41. final def getBaseMarginCol: String

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    Definition Classes
    HasBaseMarginCol
  42. final def getBaseScore: Double

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    Definition Classes
    LearningTaskParams
  43. final def getCheckpointInterval: Int

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    Definition Classes
    GeneralParams
  44. final def getCheckpointPath: String

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

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

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    Definition Classes
    BoosterParams
  47. final def getColsampleBytree: Double

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    Definition Classes
    BoosterParams
  48. final def getContribPredictionCol: String

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

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    Definition Classes
    Params
  50. final def getEta: Double

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    Definition Classes
    BoosterParams
  51. final def getEvalMetric: String

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    Definition Classes
    LearningTaskParams
  52. final def getGamma: Double

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    Definition Classes
    BoosterParams
  53. final def getGrowPolicy: String

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    Definition Classes
    BoosterParams
  54. 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

  55. 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

  56. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  57. final def getLambda: Double

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    Definition Classes
    BoosterParams
  58. final def getLambdaBias: Double

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    Definition Classes
    BoosterParams
  59. final def getLeafPredictionCol: String

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    Definition Classes
    HasLeafPredictionCol
  60. final def getMaxBins: Int

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    Definition Classes
    BoosterParams
  61. final def getMaxDeltaStep: Double

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    Definition Classes
    BoosterParams
  62. final def getMaxDepth: Int

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    Definition Classes
    BoosterParams
  63. final def getMaximizeEvaluationMetrics: Boolean

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

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    Definition Classes
    OpPipelineStageParams
  65. final def getMinChildWeight: Double

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    Definition Classes
    BoosterParams
  66. final def getMissing: Float

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    Definition Classes
    GeneralParams
  67. final def getNormalizeType: String

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    Definition Classes
    BoosterParams
  68. final def getNthread: Int

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    Definition Classes
    GeneralParams
  69. final def getNumClass: Int

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    Definition Classes
    HasNumClass
  70. final def getNumEarlyStoppingRounds: Int

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    Definition Classes
    LearningTaskParams
  71. final def getNumRound: Int

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    Definition Classes
    GeneralParams
  72. final def getNumWorkers: Int

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    Definition Classes
    GeneralParams
  73. final def getObjective: String

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    Definition Classes
    LearningTaskParams
  74. final def getObjectiveType: String

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

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    Definition Classes
    Params
  76. 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
    OpPipelineStage2OpPipelineStageBase
  77. 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
  78. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  79. final def getRateDrop: Double

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    Definition Classes
    BoosterParams
  80. final def getSampleType: String

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    Definition Classes
    BoosterParams
  81. final def getScalePosWeight: Double

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    Definition Classes
    BoosterParams
  82. final def getSeed: Long

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    Definition Classes
    GeneralParams
  83. final def getSilent: Int

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    Definition Classes
    GeneralParams
  84. final def getSketchEps: Double

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    Definition Classes
    BoosterParams
  85. final def getSkipDrop: Double

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    Definition Classes
    BoosterParams
  86. def getSparkMlStage(): Option[XGBoostClassifier]

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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
  87. 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
  88. final def getSubsample: Double

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    Definition Classes
    BoosterParams
  89. final def getTimeoutRequestWorkers: Long

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    Definition Classes
    GeneralParams
  90. final def getTrainTestRatio: Double

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    Definition Classes
    LearningTaskParams
  91. 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
  92. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  93. final def getTreeLimit: Int

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    Definition Classes
    BoosterParams
  94. final def getTreeMethod: String

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    Definition Classes
    BoosterParams
  95. final def getUseExternalMemory: Boolean

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    Definition Classes
    GeneralParams
  96. final def getWeightCol: String

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    Definition Classes
    HasWeightCol
  97. final val growPolicy: Param[String]

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

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

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    Definition Classes
    Params
  100. def hashCode(): Int

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  105. 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
  106. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  107. val inputParam2Name: String

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    Definition Classes
    OpPredictorWrapper
  108. final def isDefined(param: Param[_]): Boolean

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

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

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    Definition Classes
    Params
  111. def isTraceEnabled(): Boolean

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

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    Definition Classes
    BoosterParams
  113. final val lambdaBias: DoubleParam

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

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    Definition Classes
    HasLeafPredictionCol
  115. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  122. def logName: String

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

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

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

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

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

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    Definition Classes
    BoosterParams
  128. final val maxDeltaStep: DoubleParam

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    Definition Classes
    BoosterParams
  129. final val maxDepth: IntParam

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    Definition Classes
    BoosterParams
  130. final val maximizeEvaluationMetrics: BooleanParam

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    Definition Classes
    LearningTaskParams
  131. final val minChildWeight: DoubleParam

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    Definition Classes
    BoosterParams
  132. final val missing: FloatParam

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    Definition Classes
    GeneralParams
  133. final def ne(arg0: AnyRef): Boolean

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

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

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

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    Definition Classes
    AnyRef
  137. final val nthread: IntParam

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    Definition Classes
    GeneralParams
  138. final val numClass: IntParam

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    Definition Classes
    HasNumClass
  139. final val numEarlyStoppingRounds: IntParam

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    Definition Classes
    LearningTaskParams
  140. final val numRound: IntParam

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    Definition Classes
    GeneralParams
  141. final val numWorkers: IntParam

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

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

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    Definition Classes
    LearningTaskParams
  144. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpXGBoostClassifierOpPipelineStageBase
  146. 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
  147. 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
  148. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  149. 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
  150. val outputParamName: String

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

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    Definition Classes
    Params
  152. val predictor: XGBoostClassifier

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  153. final val rateDrop: DoubleParam

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

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

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

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    Definition Classes
    BoosterParams
  157. final val seed: LongParam

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

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

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

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    Definition Classes
    Params
  161. def setAlpha(value: Double): OpXGBoostClassifier.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]

  162. def setBaseMarginCol(value: String): OpXGBoostClassifier.this.type

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    Initial prediction (aka base margin) column name.

  163. def setBaseScore(value: Double): OpXGBoostClassifier.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

  164. def setCheckpointInterval(value: Int): OpXGBoostClassifier.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.

  165. def setCheckpointPath(value: String): OpXGBoostClassifier.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

  166. def setColsampleBylevel(value: Double): OpXGBoostClassifier.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]

  167. def setColsampleBytree(value: Double): OpXGBoostClassifier.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]

  168. def setCustomEval(value: EvalTrait): OpXGBoostClassifier.this.type

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    Customized evaluation function provided by user.

    Customized evaluation function provided by user. default: null

  169. def setCustomObj(value: ObjectiveTrait): OpXGBoostClassifier.this.type

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    Customized objective function provided by user.

    Customized objective function provided by user. default: null

  170. final def setDefault(paramPairs: ParamPair[_]*): OpXGBoostClassifier.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  172. def setEta(value: Double): OpXGBoostClassifier.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]

  173. def setEvalMetric(value: String): OpXGBoostClassifier.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

  174. def setGamma(value: Double): OpXGBoostClassifier.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]

  175. def setGrowPolicy(value: String): OpXGBoostClassifier.this.type

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    Growth policy for fast histogram algorithm

  176. final def setInput(features: InputFeatures): OpXGBoostClassifier.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
  177. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpXGBoostClassifier.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
  178. def setLambda(value: Double): OpXGBoostClassifier.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]

  179. def setLambdaBias(value: Double): OpXGBoostClassifier.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)

  180. def setMaxBins(value: Int): OpXGBoostClassifier.this.type

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    Maximum number of bins in histogram

  181. def setMaxDeltaStep(value: Double): OpXGBoostClassifier.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]

  182. def setMaxDepth(value: Int): OpXGBoostClassifier.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]

  183. final def setMetadata(m: Metadata): OpXGBoostClassifier.this.type

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    Definition Classes
    OpPipelineStageParams
  184. def setMinChildWeight(value: Double): OpXGBoostClassifier.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]

  185. def setMissing(value: Float): OpXGBoostClassifier.this.type

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    The value treated as missing

  186. def setNormalizeType(value: String): OpXGBoostClassifier.this.type

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    Parameter of Dart booster.

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

  187. def setNthread(value: Int): OpXGBoostClassifier.this.type

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    Number of threads used by per worker.

    Number of threads used by per worker. default 1

  188. def setNumClass(value: Int): OpXGBoostClassifier.this.type

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    Number of classes

  189. def setNumEarlyStoppingRounds(value: Int): OpXGBoostClassifier.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.

  190. def setNumRound(value: Int): OpXGBoostClassifier.this.type

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    The number of rounds for boosting

  191. def setNumWorkers(value: Int): OpXGBoostClassifier.this.type

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    Number of workers used to train xgboost model.

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

  192. def setObjective(value: String): OpXGBoostClassifier.this.type

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  193. def setOutputFeatureName(name: String): OpXGBoostClassifier.this.type

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    Definition Classes
    OpPipelineStage
  194. def setRateDrop(value: Double): OpXGBoostClassifier.this.type

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    Parameter of Dart booster.

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

  195. def setSampleType(value: String): OpXGBoostClassifier.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"]

  196. def setScalePosWeight(value: Double): OpXGBoostClassifier.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]

  197. def setSeed(value: Long): OpXGBoostClassifier.this.type

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

  198. def setSilent(value: Int): OpXGBoostClassifier.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

  199. def setSketchEps(value: Double): OpXGBoostClassifier.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)

  200. def setSkipDrop(value: Double): OpXGBoostClassifier.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]

  201. def setSparkMlStage(stage: Option[XGBoostClassifier]): OpXGBoostClassifier.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  202. def setStageSavePath(path: String): OpXGBoostClassifier.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
  203. def setSubsample(value: Double): OpXGBoostClassifier.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]

  204. def setTimeoutRequestWorkers(value: Long): OpXGBoostClassifier.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

  205. def setTrackerConf(value: TrackerConf): OpXGBoostClassifier.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".

  206. def setTrainTestRatio(value: Double): OpXGBoostClassifier.this.type

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    Fraction of training points to use for testing.

  207. def setTreeMethod(value: String): OpXGBoostClassifier.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']

  208. def setUseExternalMemory(value: Boolean): OpXGBoostClassifier.this.type

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    Whether to use external memory as cache.

    Whether to use external memory as cache. default: false

  209. def setWeightCol(value: String): OpXGBoostClassifier.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.

  210. final val silent: IntParam

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    Definition Classes
    GeneralParams
  211. final val sketchEps: DoubleParam

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    Definition Classes
    BoosterParams
  212. final val skipDrop: DoubleParam

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    Definition Classes
    BoosterParams
  213. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  214. final val sparkMlStage: SparkStageParam[XGBoostClassifier]

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    Definition Classes
    SparkWrapperParams
  215. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  216. 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
  217. final val subsample: DoubleParam

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

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    Definition Classes
    AnyRef
  219. final val timeoutRequestWorkers: LongParam

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    Definition Classes
    GeneralParams
  220. def toString(): String

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

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    Definition Classes
    GeneralParams
  222. final val trainTestRatio: DoubleParam

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    Definition Classes
    LearningTaskParams
  223. 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
  224. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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

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

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

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

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  230. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  231. val uid: String

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    stage uid

    stage uid

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

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

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

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

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

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    Definition Classes
    HasWeightCol
  237. final def write: MLWriter

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from OpXGBoostClassifierParams

Inherited from XGBoostClassifierParams

Inherited from HasContribPredictionCol

Inherited from HasLeafPredictionCol

Inherited from ParamMapFuncs

Inherited from HasNumClass

Inherited from HasBaseMarginCol

Inherited from HasWeightCol

Inherited from BoosterParams

Inherited from LearningTaskParams

Inherited from GeneralParams

Inherited from OpPredictorWrapper[XGBoostClassifier, XGBoostClassificationModel]

Inherited from SparkWrapperParams[XGBoostClassifier]

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[XGBoostClassificationModel]]

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