Class

com.salesforce.op.stages.impl.classification

OpRandomForestClassifier

Related Doc: package classification

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class OpRandomForestClassifier extends OpPredictorWrapper[RandomForestClassifier, RandomForestClassificationModel] with OpRandomForestClassifierParams

Wrapper for spark Random Forest Classifier org.apache.spark.ml.classification.RandomForestClassifier

Linear Supertypes
OpRandomForestClassifierParams, ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassifierParams, HasRawPredictionCol, RandomForestClassifierParams, TreeClassifierParams, RandomForestParams, TreeEnsembleParams, DecisionTreeParams, HasSeed, HasCheckpointInterval, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[RandomForestClassifier, RandomForestClassificationModel], SparkWrapperParams[RandomForestClassifier], OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[RandomForestClassificationModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpRandomForestClassifier
  2. OpRandomForestClassifierParams
  3. ProbabilisticClassifierParams
  4. HasThresholds
  5. HasProbabilityCol
  6. ClassifierParams
  7. HasRawPredictionCol
  8. RandomForestClassifierParams
  9. TreeClassifierParams
  10. RandomForestParams
  11. TreeEnsembleParams
  12. DecisionTreeParams
  13. HasSeed
  14. HasCheckpointInterval
  15. PredictorParams
  16. HasPredictionCol
  17. HasFeaturesCol
  18. HasLabelCol
  19. OpPredictorWrapper
  20. SparkWrapperParams
  21. OpPipelineStage2
  22. HasIn2
  23. HasIn1
  24. OpPipelineStage
  25. OpPipelineStageBase
  26. MLWritable
  27. OpPipelineStageParams
  28. InputParams
  29. Estimator
  30. PipelineStage
  31. Logging
  32. Params
  33. Serializable
  34. Serializable
  35. Identifiable
  36. AnyRef
  37. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

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    uid

    stage uid

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. final def asInstanceOf[T0]: T0

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    Definition Classes
    Any
  6. final val cacheNodeIds: BooleanParam

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    Definition Classes
    DecisionTreeParams
  7. 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
  8. 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
  9. final val checkpointInterval: IntParam

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    Definition Classes
    HasCheckpointInterval
  10. final def clear(param: Param[_]): OpRandomForestClassifier.this.type

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    Definition Classes
    Params
  11. def clone(): AnyRef

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

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

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

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

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

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    Definition Classes
    Params
  18. def explainParams(): String

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

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

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    Definition Classes
    Params
  21. final val featureSubsetStrategy: Param[String]

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    Definition Classes
    TreeEnsembleParams
  22. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit

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

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    Function that fits the binary model

    Function that fits the binary model

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

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

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

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    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  28. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params
  29. final def getCacheNodeIds: Boolean

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    Definition Classes
    DecisionTreeParams
  30. final def getCheckpointInterval: Int

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    Definition Classes
    HasCheckpointInterval
  31. final def getClass(): Class[_]

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

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    Definition Classes
    Params
  33. final def getFeatureSubsetStrategy: String

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    Definition Classes
    TreeEnsembleParams
  34. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  35. final def getImpurity: String

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    Definition Classes
    TreeClassifierParams
  36. 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

  37. 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

  38. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  39. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  40. final def getMaxBins: Int

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    Definition Classes
    DecisionTreeParams
  41. final def getMaxDepth: Int

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    Definition Classes
    DecisionTreeParams
  42. final def getMaxMemoryInMB: Int

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    Definition Classes
    DecisionTreeParams
  43. final def getMetadata(): Metadata

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    Definition Classes
    OpPipelineStageParams
  44. final def getMinInfoGain: Double

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    Definition Classes
    DecisionTreeParams
  45. final def getMinInstancesPerNode: Int

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    Definition Classes
    DecisionTreeParams
  46. final def getNumTrees: Int

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    Definition Classes
    RandomForestParams
  47. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  48. 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
  49. 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
  50. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  51. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  52. final def getProbabilityCol: String

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    Definition Classes
    HasProbabilityCol
  53. final def getRawPredictionCol: String

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    Definition Classes
    HasRawPredictionCol
  54. final def getSeed: Long

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    Definition Classes
    HasSeed
  55. def getSparkMlStage(): Option[RandomForestClassifier]

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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
  56. 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
  57. final def getSubsamplingRate: Double

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    Definition Classes
    TreeEnsembleParams
  58. def getThresholds: Array[Double]

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    Definition Classes
    HasThresholds
  59. 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
  60. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

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

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

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    Definition Classes
    Params
  63. def hashCode(): Int

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    Definition Classes
    AnyRef → Any
  64. final val impurity: Param[String]

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    Definition Classes
    TreeClassifierParams
  65. final def in1: TransientFeature

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  69. 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
  70. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  71. val inputParam2Name: String

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

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

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

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    Definition Classes
    Params
  75. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging
  76. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol
  77. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  84. def logName: String

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

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

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

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

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

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    Definition Classes
    DecisionTreeParams
  90. final val maxDepth: IntParam

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    Definition Classes
    DecisionTreeParams
  91. final val maxMemoryInMB: IntParam

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    Definition Classes
    DecisionTreeParams
  92. final val minInfoGain: DoubleParam

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    Definition Classes
    DecisionTreeParams
  93. final val minInstancesPerNode: IntParam

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    Definition Classes
    DecisionTreeParams
  94. final def ne(arg0: AnyRef): Boolean

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

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

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    Definition Classes
    AnyRef
  97. final val numTrees: IntParam

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    Definition Classes
    RandomForestParams
  98. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpRandomForestClassifierOpPipelineStageBase
  100. 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
  101. 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
  102. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  103. 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
  104. val outputParamName: String

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

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    Definition Classes
    Params
  106. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol
  107. val predictor: RandomForestClassifier

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  108. final val probabilityCol: Param[String]

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    Definition Classes
    HasProbabilityCol
  109. final val rawPredictionCol: Param[String]

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    Definition Classes
    HasRawPredictionCol
  110. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  111. final val seed: LongParam

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    Definition Classes
    HasSeed
  112. final def set(paramPair: ParamPair[_]): OpRandomForestClassifier.this.type

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

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

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    Definition Classes
    Params
  115. def setCacheNodeIds(value: Boolean): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  116. def setCheckpointInterval(value: Int): OpRandomForestClassifier.this.type

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    Specifies how often to checkpoint the cached node IDs.

    Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in org.apache.spark.SparkContext. Must be at least 1. (default = 10)

    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  117. final def setDefault(paramPairs: ParamPair[_]*): OpRandomForestClassifier.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  119. def setFeatureSubsetStrategy(value: String): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → TreeEnsembleParams
  120. def setImpurity(value: String): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → TreeClassifierParams
  121. final def setInput(features: InputFeatures): OpRandomForestClassifier.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
  122. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpRandomForestClassifier.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
  123. def setMaxBins(value: Int): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  124. def setMaxDepth(value: Int): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  125. def setMaxMemoryInMB(value: Int): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  126. final def setMetadata(m: Metadata): OpRandomForestClassifier.this.type

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    Definition Classes
    OpPipelineStageParams
  127. def setMinInfoGain(value: Double): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  128. def setMinInstancesPerNode(value: Int): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  129. def setNumTrees(value: Int): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → RandomForestParams
  130. def setOutputFeatureName(name: String): OpRandomForestClassifier.this.type

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    Definition Classes
    OpPipelineStage
  131. def setSeed(value: Long): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → DecisionTreeParams
  132. def setSparkMlStage(stage: Option[RandomForestClassifier]): OpRandomForestClassifier.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  133. def setStageSavePath(path: String): OpRandomForestClassifier.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
  134. def setSubsamplingRate(value: Double): OpRandomForestClassifier.this.type

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    Definition Classes
    OpRandomForestClassifier → TreeEnsembleParams
  135. def setThresholds(value: Array[Double]): OpRandomForestClassifier.this.type

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    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

  136. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  137. final val sparkMlStage: SparkStageParam[RandomForestClassifier]

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    Definition Classes
    SparkWrapperParams
  138. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  139. 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
  140. final val subsamplingRate: DoubleParam

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    Definition Classes
    TreeEnsembleParams
  141. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  142. final val thresholds: DoubleArrayParam

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    Definition Classes
    HasThresholds
  143. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  144. 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
  145. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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

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

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

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  150. val uid: String

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    stage uid

    stage uid

    Definition Classes
    OpPredictorWrapper → Identifiable
  151. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  152. final def wait(): Unit

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

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

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

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from RandomForestClassifierParams

Inherited from TreeClassifierParams

Inherited from RandomForestParams

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[RandomForestClassifier, RandomForestClassificationModel]

Inherited from SparkWrapperParams[RandomForestClassifier]

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[RandomForestClassificationModel]]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

expertSetParam

param

setParam

Ungrouped