Class

com.salesforce.op.stages.impl.classification

OpXGBoostClassificationModel

Related Doc: package classification

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class OpXGBoostClassificationModel extends OpProbabilisticClassifierModel[XGBoostClassificationModel]

Class that takes in a spark XGBoostClassificationModel and wraps it into an OP model which returns a Prediction feature

Linear Supertypes
OpProbabilisticClassifierModel[XGBoostClassificationModel], OpPredictorWrapperModel[XGBoostClassificationModel], OpTransformer2[RealNN, OPVector, Prediction], OpTransformer, OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, SparkWrapperParams[XGBoostClassificationModel], Model[OpPredictorWrapperModel[XGBoostClassificationModel]], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpXGBoostClassificationModel
  2. OpProbabilisticClassifierModel
  3. OpPredictorWrapperModel
  4. OpTransformer2
  5. OpTransformer
  6. OpPipelineStage2
  7. HasIn2
  8. HasIn1
  9. OpPipelineStage
  10. OpPipelineStageBase
  11. MLWritable
  12. OpPipelineStageParams
  13. InputParams
  14. SparkWrapperParams
  15. Model
  16. Transformer
  17. PipelineStage
  18. Logging
  19. Params
  20. Serializable
  21. Serializable
  22. Identifiable
  23. AnyRef
  24. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new OpXGBoostClassificationModel(sparkModel: XGBoostClassificationModel, uid: String = UID[OpXGBoostClassificationModel], operationName: String = ...)(implicit tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN], tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector], tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction], ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]])

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    sparkModel

    model to wrap

    uid

    uid to give stage

    operationName

    unique name of the operation this stage performs

Type Members

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

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    Input Features type

    Input Features type

    Definition Classes
    OpPipelineStage2OpPipelineStageInputParams
  2. type KeyValue = (String) ⇒ Any

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    Feature name (key) -> value lookup, e.g Row, Map etc.

    Feature name (key) -> value lookup, e.g Row, Map etc.

    Definition Classes
    OpTransformer
  3. 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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. 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
  7. final 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
    OpTransformer2OpPipelineStageBase
  8. final def clear(param: Param[_]): OpXGBoostClassificationModel.this.type

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    Definition Classes
    Params
  9. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  10. final def copy(extra: ParamMap): OpXGBoostClassificationModel.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
  11. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

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

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

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    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String

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    Definition Classes
    Params
  16. def explainParams(): String

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

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

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    Definition Classes
    Params
  19. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  20. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  21. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any
  22. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  23. 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

  24. 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

  25. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  26. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  27. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  28. 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
  29. 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
  30. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  31. def getSparkMlStage(): Option[XGBoostClassificationModel]

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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
  32. 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
  33. 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
  34. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  35. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  37. def hasParent: Boolean

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    Definition Classes
    Model
  38. def hashCode(): Int

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  43. 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
  44. final def isDefined(param: Param[_]): Boolean

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

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

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    Definition Classes
    Params
  47. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  48. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  55. def logName: String

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

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    Attributes
    protected
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    Logging
  57. def logTrace(msg: ⇒ String): Unit

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

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    Attributes
    protected
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    Logging
  59. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
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    Logging
  60. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef
  61. final def notify(): Unit

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

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    Definition Classes
    AnyRef
  63. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpPipelineStageBase
  65. 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
    OpPredictorWrapperModelOpPipelineStageBase
  66. 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
  67. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  68. 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
  69. lazy val params: Array[Param[_]]

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    Definition Classes
    Params
  70. var parent: Estimator[OpPredictorWrapperModel[XGBoostClassificationModel]]

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    Definition Classes
    Model
  71. def predictRaw(features: Vector): Vector

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    Attributes
    protected
    Definition Classes
    OpProbabilisticClassifierModel
  72. def predictRawMirror: scala.reflect.api.JavaUniverse.MethodMirror

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    Attributes
    protected
    Definition Classes
    OpXGBoostClassificationModelOpProbabilisticClassifierModel
  73. def probability2prediction(prob: Vector): Double

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    Attributes
    protected
    Definition Classes
    OpProbabilisticClassifierModel
  74. lazy val probability2predictionMirror: scala.reflect.api.JavaUniverse.MethodMirror

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  75. def raw2probability(raw: Vector): Vector

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    Attributes
    protected
    Definition Classes
    OpProbabilisticClassifierModel
  76. def raw2probabilityMirror: scala.reflect.api.JavaUniverse.MethodMirror

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    Attributes
    protected
    Definition Classes
    OpXGBoostClassificationModelOpProbabilisticClassifierModel
  77. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  78. final def set(paramPair: ParamPair[_]): OpXGBoostClassificationModel.this.type

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    Params
  79. final def set(param: String, value: Any): OpXGBoostClassificationModel.this.type

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    protected
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    Params
  80. final def set[T](param: Param[T], value: T): OpXGBoostClassificationModel.this.type

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    Definition Classes
    Params
  81. final def setDefault(paramPairs: ParamPair[_]*): OpXGBoostClassificationModel.this.type

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    Attributes
    protected
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    Params
  82. final def setDefault[T](param: Param[T], value: T): OpXGBoostClassificationModel.this.type

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    Attributes
    protected
    Definition Classes
    Params
  83. final def setInput(features: InputFeatures): OpXGBoostClassificationModel.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
  84. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpXGBoostClassificationModel.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
  85. final def setMetadata(m: Metadata): OpXGBoostClassificationModel.this.type

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    Definition Classes
    OpPipelineStageParams
  86. def setOutputFeatureName(name: String): OpXGBoostClassificationModel.this.type

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    Definition Classes
    OpPipelineStage
  87. def setParent(parent: Estimator[OpPredictorWrapperModel[XGBoostClassificationModel]]): OpPredictorWrapperModel[XGBoostClassificationModel]

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    Definition Classes
    Model
  88. def setSparkMlStage(stage: Option[XGBoostClassificationModel]): OpXGBoostClassificationModel.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  89. def setStageSavePath(path: String): OpXGBoostClassificationModel.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
  90. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  91. final val sparkMlStage: SparkStageParam[XGBoostClassificationModel]

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    Definition Classes
    SparkWrapperParams
  92. val sparkModel: XGBoostClassificationModel

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    Definition Classes
    OpPredictorWrapperModel
  93. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  94. 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
  95. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  96. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  97. def transform(dataset: Dataset[_]): DataFrame

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    Spark operation on dataset to produce new output feature column using defined function

    Spark operation on dataset to produce new output feature column using defined function

    dataset

    input data for this stage

    returns

    a new dataset containing a column for the transformed feature

    Definition Classes
    OpTransformer2 → Transformer
  98. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )
  99. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()
  100. def transformFn: (RealNN, OPVector) ⇒ Prediction

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    Function used to convert input to output

    Function used to convert input to output

    Definition Classes
    OpXGBoostClassificationModelOpProbabilisticClassifierModelOpTransformer2
  101. lazy val transformKeyValue: (KeyValue) ⇒ Any

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    Creates a transform function to transform any key/value to a value

    Creates a transform function to transform any key/value to a value

    returns

    a transform function to transform any key/value to a value

    Definition Classes
    OpTransformer2 → OpTransformer
  102. def transformMap: (Map[String, Any]) ⇒ Any

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    Creates a transform function to transform Map to a value

    Creates a transform function to transform Map to a value

    returns

    a transform function to transform Map to a value

    Definition Classes
    OpTransformer
  103. def transformRow: (Row) ⇒ Any

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    Creates a transform function to transform Row to a value

    Creates a transform function to transform Row to a value

    returns

    a transform function to transform Row to a value

    Definition Classes
    OpTransformer
  104. 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
  105. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  106. implicit val tti1: scala.reflect.api.JavaUniverse.TypeTag[RealNN]

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  107. implicit val tti2: scala.reflect.api.JavaUniverse.TypeTag[OPVector]

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  108. implicit val tto: scala.reflect.api.JavaUniverse.TypeTag[Prediction]

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  109. implicit val ttov: scala.reflect.api.JavaUniverse.TypeTag[Map[String, Double]]

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  110. val uid: String

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    Definition Classes
    OpPredictorWrapperModel → Identifiable
  111. final def wait(): Unit

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

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

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  114. final def write: MLWriter

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from OpProbabilisticClassifierModel[XGBoostClassificationModel]

Inherited from OpPredictorWrapperModel[XGBoostClassificationModel]

Inherited from OpTransformer

Inherited from HasIn2

Inherited from HasIn1

Inherited from OpPipelineStage[Prediction]

Inherited from OpPipelineStageBase

Inherited from MLWritable

Inherited from OpPipelineStageParams

Inherited from InputParams

Inherited from SparkWrapperParams[XGBoostClassificationModel]

Inherited from Model[OpPredictorWrapperModel[XGBoostClassificationModel]]

Inherited from Transformer

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