Packages

class StackingRegressor extends Predictor[Vector, StackingRegressor, StackingRegressionModel] with StackingRegressorParams with MLWritable

Source
StackingRegressor.scala
Linear Supertypes
MLWritable, StackingRegressorParams, StackingParams[EnsembleRegressorType], HasBaseLearners[EnsembleRegressorType], HasStacker[EnsembleRegressorType], HasBaseLearner[EnsembleRegressorType], HasWeightCol, HasParallelism, Predictor[Vector, StackingRegressor, StackingRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[StackingRegressionModel], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. StackingRegressor
  2. MLWritable
  3. StackingRegressorParams
  4. StackingParams
  5. HasBaseLearners
  6. HasStacker
  7. HasBaseLearner
  8. HasWeightCol
  9. HasParallelism
  10. Predictor
  11. PredictorParams
  12. HasPredictionCol
  13. HasFeaturesCol
  14. HasLabelCol
  15. Estimator
  16. PipelineStage
  17. Logging
  18. Params
  19. Serializable
  20. Identifiable
  21. AnyRef
  22. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new StackingRegressor()
  2. new StackingRegressor(uid: String)

Value Members

  1. final def !=(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  2. final def ##: Int
    Definition Classes
    AnyRef → Any
  3. final def $[T](param: Param[T]): T
    Attributes
    protected
    Definition Classes
    Params
  4. final def ==(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  5. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  6. val baseLearner: Param[EnsembleRegressorType]

    param for the estimator that will be used by the ensemble learner as a base learner

    param for the estimator that will be used by the ensemble learner as a base learner

    Definition Classes
    HasBaseLearner
  7. val baseLearners: Param[Array[EnsembleRegressorType]]

    param for the estimators that will be used by the ensemble learner as base learners

    param for the estimators that will be used by the ensemble learner as base learners

    Definition Classes
    HasBaseLearners
  8. final def clear(param: Param[_]): StackingRegressor.this.type
    Definition Classes
    Params
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  10. def copy(extra: ParamMap): StackingRegressor
    Definition Classes
    StackingRegressor → Predictor → Estimator → PipelineStage → Params
  11. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  16. def explainParams(): String
    Definition Classes
    Params
  17. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  18. def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  19. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
    Attributes
    protected
    Definition Classes
    Predictor
  20. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  22. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  24. def fit(dataset: Dataset[_]): StackingRegressionModel
    Definition Classes
    Predictor → Estimator
  25. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[StackingRegressionModel]
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  26. def fit(dataset: Dataset[_], paramMap: ParamMap): StackingRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  27. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): StackingRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0") @varargs()
  28. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  29. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  30. def getBaseLearner: EnsembleRegressorType

    Definition Classes
    HasBaseLearner
  31. def getBaseLearners: Array[EnsembleRegressorType]

    Definition Classes
    HasBaseLearners
  32. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  33. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  34. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  35. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  36. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  37. def getParallelism: Int
    Definition Classes
    HasParallelism
  38. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  39. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  40. def getStacker: EnsembleRegressorType

    Definition Classes
    HasStacker
  41. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  42. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  43. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  44. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  46. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  48. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  49. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  50. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  52. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  65. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  66. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  67. val parallelism: IntParam
    Definition Classes
    HasParallelism
  68. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  69. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  70. def save(path: String): Unit
    Definition Classes
    MLWritable
    Annotations
    @Since("1.6.0") @throws("If the input path already exists but overwrite is not enabled.")
  71. final def set(paramPair: ParamPair[_]): StackingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  72. final def set(param: String, value: Any): StackingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  73. final def set[T](param: Param[T], value: T): StackingRegressor.this.type
    Definition Classes
    Params
  74. def setBaseLearners(value: Array[EnsembleRegressorType]): StackingRegressor.this.type
  75. final def setDefault(paramPairs: ParamPair[_]*): StackingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  76. final def setDefault[T](param: Param[T], value: T): StackingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  77. def setFeaturesCol(value: String): StackingRegressor
    Definition Classes
    Predictor
  78. def setLabelCol(value: String): StackingRegressor
    Definition Classes
    Predictor
  79. def setParallelism(value: Int): StackingRegressor.this.type
  80. def setPredictionCol(value: String): StackingRegressor
    Definition Classes
    Predictor
  81. def setStacker(value: EnsembleRegressorType): StackingRegressor.this.type
  82. val stacker: Param[EnsembleRegressorType]

    param for the estimator that will be used by the ensemble learner to aggregate results of base learner(s)

    param for the estimator that will be used by the ensemble learner to aggregate results of base learner(s)

    Definition Classes
    HasStacker
  83. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  84. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  85. def train(dataset: Dataset[_]): StackingRegressionModel
    Attributes
    protected
    Definition Classes
    StackingRegressor → Predictor
  86. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  87. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  88. val uid: String
    Definition Classes
    StackingRegressor → Identifiable
  89. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  90. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  91. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  92. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  93. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  94. def write: MLWriter
    Definition Classes
    StackingRegressor → MLWritable

Inherited from MLWritable

Inherited from StackingRegressorParams

Inherited from StackingParams[EnsembleRegressorType]

Inherited from HasBaseLearners[EnsembleRegressorType]

Inherited from HasStacker[EnsembleRegressorType]

Inherited from HasBaseLearner[EnsembleRegressorType]

Inherited from HasWeightCol

Inherited from HasParallelism

Inherited from Predictor[Vector, StackingRegressor, StackingRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[StackingRegressionModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

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