Class

com.salesforce.op.stages.impl.regression

OpDecisionTreeRegressor

Related Doc: package regression

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class OpDecisionTreeRegressor extends OpPredictorWrapper[DecisionTreeRegressor, DecisionTreeRegressionModel] with OpDecisionTreeRegressorParams

Wrapper for spark Decision Tree Regressor org.apache.spark.ml.regression.DecisionTreeRegressor

Linear Supertypes
OpDecisionTreeRegressorParams, DecisionTreeRegressorParams, HasVarianceCol, TreeRegressorParams, DecisionTreeParams, HasSeed, HasCheckpointInterval, PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, OpPredictorWrapper[DecisionTreeRegressor, DecisionTreeRegressionModel], SparkWrapperParams[DecisionTreeRegressor], OpPipelineStage2[RealNN, OPVector, Prediction], HasIn2, HasIn1, OpPipelineStage[Prediction], OpPipelineStageBase, MLWritable, OpPipelineStageParams, InputParams, Estimator[OpPredictorWrapperModel[DecisionTreeRegressionModel]], PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. OpDecisionTreeRegressor
  2. OpDecisionTreeRegressorParams
  3. DecisionTreeRegressorParams
  4. HasVarianceCol
  5. TreeRegressorParams
  6. DecisionTreeParams
  7. HasSeed
  8. HasCheckpointInterval
  9. PredictorParams
  10. HasPredictionCol
  11. HasFeaturesCol
  12. HasLabelCol
  13. OpPredictorWrapper
  14. SparkWrapperParams
  15. OpPipelineStage2
  16. HasIn2
  17. HasIn1
  18. OpPipelineStage
  19. OpPipelineStageBase
  20. MLWritable
  21. OpPipelineStageParams
  22. InputParams
  23. Estimator
  24. PipelineStage
  25. Logging
  26. Params
  27. Serializable
  28. Serializable
  29. Identifiable
  30. AnyRef
  31. Any
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Visibility
  1. Public
  2. All

Instance Constructors

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

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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[_]): OpDecisionTreeRegressor.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): OpDecisionTreeRegressor.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 featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol
  22. def finalize(): Unit

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

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    Function that fits the binary model

    Function that fits the binary model

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

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

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

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

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    Definition Classes
    Params
  28. final def getCacheNodeIds: Boolean

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    Definition Classes
    DecisionTreeParams
  29. final def getCheckpointInterval: Int

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

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

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    Definition Classes
    Params
  32. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol
  33. final def getImpurity: String

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    Definition Classes
    TreeRegressorParams
  34. 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

  35. 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

  36. final def getInputSchema(): StructType

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    Definition Classes
    OpPipelineStageParams
  37. final def getLabelCol: String

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    Definition Classes
    HasLabelCol
  38. final def getMaxBins: Int

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    Definition Classes
    DecisionTreeParams
  39. final def getMaxDepth: Int

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    Definition Classes
    DecisionTreeParams
  40. final def getMaxMemoryInMB: Int

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

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    Definition Classes
    OpPipelineStageParams
  42. final def getMinInfoGain: Double

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    Definition Classes
    DecisionTreeParams
  43. final def getMinInstancesPerNode: Int

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    Definition Classes
    DecisionTreeParams
  44. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params
  45. 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
  46. 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
  47. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params
  48. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol
  49. final def getSeed: Long

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    Definition Classes
    HasSeed
  50. def getSparkMlStage(): Option[DecisionTreeRegressor]

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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
  51. 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
  52. 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
  53. final def getTransientFeatures(): Array[TransientFeature]

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    Gets the input Features

    Gets the input Features

    returns

    input features

    Definition Classes
    InputParams
  54. final def getVarianceCol: String

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    Definition Classes
    HasVarianceCol
  55. final def hasDefault[T](param: Param[T]): Boolean

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

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    Definition Classes
    Params
  57. def hashCode(): Int

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

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    Definition Classes
    TreeRegressorParams
  59. final def in1: TransientFeature

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  63. 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
  64. val inputParam1Name: String

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    Definition Classes
    OpPredictorWrapper
  65. val inputParam2Name: String

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

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

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

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    Definition Classes
    Params
  69. def isTraceEnabled(): Boolean

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

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    Definition Classes
    HasLabelCol
  71. def log: Logger

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

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

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

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

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

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

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    Attributes
    protected
    Definition Classes
    Logging
  78. def logName: String

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

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

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

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

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

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    Definition Classes
    DecisionTreeParams
  84. final val maxDepth: IntParam

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    Definition Classes
    DecisionTreeParams
  85. final val maxMemoryInMB: IntParam

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    Definition Classes
    DecisionTreeParams
  86. final val minInfoGain: DoubleParam

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    Definition Classes
    DecisionTreeParams
  87. final val minInstancesPerNode: IntParam

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

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

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

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    Definition Classes
    AnyRef
  91. def onGetMetadata(): Unit

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    Function to be called on getMetadata

    Function to be called on getMetadata

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

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    Function to be called on setInput

    Function to be called on setInput

    Attributes
    protected
    Definition Classes
    OpDecisionTreeRegressorOpPipelineStageBase
  93. 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
  94. 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
  95. def outputFeatureUid: String

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    Attributes
    protected[com.salesforce.op]
    Definition Classes
    OpPipelineStage2OpPipelineStage
  96. 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
  97. val outputParamName: String

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

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

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    Definition Classes
    HasPredictionCol
  100. val predictor: DecisionTreeRegressor

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    the predictor to wrap

    the predictor to wrap

    Definition Classes
    OpPredictorWrapper
  101. def save(path: String): Unit

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

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

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

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

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    Definition Classes
    Params
  106. def setCacheNodeIds(value: Boolean): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  107. def setCheckpointInterval(value: Int): OpDecisionTreeRegressor.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
    OpDecisionTreeRegressor → DecisionTreeParams
  108. final def setDefault(paramPairs: ParamPair[_]*): OpDecisionTreeRegressor.this.type

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

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    Attributes
    protected
    Definition Classes
    Params
  110. def setImpurity(value: String): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → TreeRegressorParams
  111. final def setInput(features: InputFeatures): OpDecisionTreeRegressor.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
  112. final def setInputFeatures[S <: OPFeature](features: Array[S]): OpDecisionTreeRegressor.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
  113. def setMaxBins(value: Int): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  114. def setMaxDepth(value: Int): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  115. def setMaxMemoryInMB(value: Int): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  116. final def setMetadata(m: Metadata): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpPipelineStageParams
  117. def setMinInfoGain(value: Double): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  118. def setMinInstancesPerNode(value: Int): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  119. def setOutputFeatureName(name: String): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpPipelineStage
  120. def setSeed(value: Long): OpDecisionTreeRegressor.this.type

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    Definition Classes
    OpDecisionTreeRegressor → DecisionTreeParams
  121. def setSparkMlStage(stage: Option[DecisionTreeRegressor]): OpDecisionTreeRegressor.this.type

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    Attributes
    protected
    Definition Classes
    SparkWrapperParams
  122. def setStageSavePath(path: String): OpDecisionTreeRegressor.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
  123. def setVarianceCol(value: String): OpDecisionTreeRegressor.this.type

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  124. final val sparkInputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  125. final val sparkMlStage: SparkStageParam[DecisionTreeRegressor]

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    Definition Classes
    SparkWrapperParams
  126. final val sparkOutputColParamNames: StringArrayParam

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    Definition Classes
    SparkWrapperParams
  127. 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
  128. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef
  129. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any
  130. 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
  131. def transformSchema(schema: StructType, logging: Boolean): StructType

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

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

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

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

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    Definition Classes
    OpPredictorWrapperOpPipelineStage2
  136. val uid: String

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    stage uid

    stage uid

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

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    Attributes
    protected
    Definition Classes
    DecisionTreeRegressorParams → PredictorParams
  138. final val varianceCol: Param[String]

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    Definition Classes
    HasVarianceCol
  139. final def wait(): Unit

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

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

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

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    Definition Classes
    OpPipelineStageBase → MLWritable

Inherited from DecisionTreeRegressorParams

Inherited from HasVarianceCol

Inherited from TreeRegressorParams

Inherited from DecisionTreeParams

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from OpPredictorWrapper[DecisionTreeRegressor, DecisionTreeRegressionModel]

Inherited from SparkWrapperParams[DecisionTreeRegressor]

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[DecisionTreeRegressionModel]]

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

setParam

Ungrouped