Class/Object

org.apache.spark.ml.regression

StackingRegressionModel

Related Docs: object StackingRegressionModel | package regression

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class StackingRegressionModel extends PredictionModel[Vector, StackingRegressionModel] with StackingRegressorParams with MLWritable with Serializable

Linear Supertypes
MLWritable, StackingRegressorParams, StackingParams, HasBaseLearners, HasStacker, HasWeightCol, HasParallelism, PredictionModel[Vector, StackingRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[StackingRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. StackingRegressionModel
  2. MLWritable
  3. StackingRegressorParams
  4. StackingParams
  5. HasBaseLearners
  6. HasStacker
  7. HasWeightCol
  8. HasParallelism
  9. PredictionModel
  10. PredictorParams
  11. HasPredictionCol
  12. HasFeaturesCol
  13. HasLabelCol
  14. Model
  15. Transformer
  16. PipelineStage
  17. Logging
  18. Params
  19. Serializable
  20. Serializable
  21. Identifiable
  22. AnyRef
  23. Any
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Visibility
  1. Public
  2. All

Instance Constructors

  1. new StackingRegressionModel(models: Array[EnsemblePredictionModelType], stack: EnsemblePredictionModelType)

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  2. new StackingRegressionModel(uid: String, models: Array[EnsemblePredictionModelType], stack: EnsemblePredictionModelType)

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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. val baseLearners: Param[Array[EnsemblePredictorType]]

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    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

  7. final def clear(param: Param[_]): StackingRegressionModel.this.type

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    Definition Classes
    Params

  8. def clone(): AnyRef

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  9. def copy(extra: ParamMap): StackingRegressionModel

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    Definition Classes
    StackingRegressionModel → Model → Transformer → PipelineStage → Params

  10. def copyValues[T <: Params](to: T, extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  11. final def defaultCopy[T <: Params](extra: ParamMap): T

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    Attributes
    protected
    Definition Classes
    Params

  12. final def eq(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  13. def equals(arg0: Any): Boolean

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    Definition Classes
    AnyRef → Any

  14. def explainParam(param: Param[_]): String

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    Definition Classes
    Params

  15. def explainParams(): String

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    Definition Classes
    Params

  16. final def extractParamMap(): ParamMap

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    Definition Classes
    Params

  17. final def extractParamMap(extra: ParamMap): ParamMap

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    Definition Classes
    Params

  18. final val featuresCol: Param[String]

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    Definition Classes
    HasFeaturesCol

  19. def featuresDataType: DataType

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    Attributes
    protected
    Definition Classes
    PredictionModel

  20. def finalize(): Unit

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    Attributes
    protected[java.lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )

  21. def fitBaseLearner(baseLearner: EnsemblePredictorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType

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    Definition Classes
    HasBaseLearners

  22. final def get[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  23. def getBaseLearners: Array[EnsemblePredictorType]

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    Definition Classes
    HasBaseLearners

  24. final def getClass(): Class[_]

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    Definition Classes
    AnyRef → Any

  25. final def getDefault[T](param: Param[T]): Option[T]

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    Definition Classes
    Params

  26. final def getFeaturesCol: String

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    Definition Classes
    HasFeaturesCol

  27. final def getLabelCol: String

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    Definition Classes
    HasLabelCol

  28. final def getOrDefault[T](param: Param[T]): T

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    Definition Classes
    Params

  29. def getParallelism: Int

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    Definition Classes
    HasParallelism

  30. def getParam(paramName: String): Param[Any]

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    Definition Classes
    Params

  31. final def getPredictionCol: String

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    Definition Classes
    HasPredictionCol

  32. def getStacker: EnsemblePredictorType

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    Definition Classes
    HasStacker

  33. final def getWeightCol: String

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    Definition Classes
    HasWeightCol

  34. final def hasDefault[T](param: Param[T]): Boolean

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    Definition Classes
    Params

  35. def hasParam(paramName: String): Boolean

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    Definition Classes
    Params

  36. def hasParent: Boolean

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    Definition Classes
    Model

  37. def hashCode(): Int

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    Definition Classes
    AnyRef → Any

  38. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  39. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  40. final def isDefined(param: Param[_]): Boolean

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    Definition Classes
    Params

  41. final def isInstanceOf[T0]: Boolean

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    Definition Classes
    Any

  42. final def isSet(param: Param[_]): Boolean

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    Definition Classes
    Params

  43. def isTraceEnabled(): Boolean

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    Attributes
    protected
    Definition Classes
    Logging

  44. final val labelCol: Param[String]

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    Definition Classes
    HasLabelCol

  45. def log: Logger

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    Attributes
    protected
    Definition Classes
    Logging

  46. def logDebug(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  47. def logDebug(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  48. def logError(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  49. def logError(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  50. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  51. def logInfo(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  52. def logName: String

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    Attributes
    protected
    Definition Classes
    Logging

  53. def logTrace(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  54. def logTrace(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  55. def logWarning(msg: ⇒ String, throwable: Throwable): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  56. def logWarning(msg: ⇒ String): Unit

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    Attributes
    protected
    Definition Classes
    Logging

  57. val models: Array[EnsemblePredictionModelType]

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  58. final def ne(arg0: AnyRef): Boolean

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    Definition Classes
    AnyRef

  59. final def notify(): Unit

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    Definition Classes
    AnyRef

  60. final def notifyAll(): Unit

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    Definition Classes
    AnyRef

  61. def numFeatures: Int

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    Definition Classes
    PredictionModel
    Annotations
    @Since( "1.6.0" )

  62. val parallelism: IntParam

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    Definition Classes
    HasParallelism

  63. lazy val params: Array[Param[_]]

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    Definition Classes
    Params

  64. var parent: Estimator[StackingRegressionModel]

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    Definition Classes
    Model

  65. def predict(features: Vector): Double

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    Definition Classes
    StackingRegressionModel → PredictionModel

  66. final val predictionCol: Param[String]

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    Definition Classes
    HasPredictionCol

  67. def save(path: String): Unit

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    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )

  68. final def set(paramPair: ParamPair[_]): StackingRegressionModel.this.type

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    Attributes
    protected
    Definition Classes
    Params

  69. final def set(param: String, value: Any): StackingRegressionModel.this.type

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    Attributes
    protected
    Definition Classes
    Params

  70. final def set[T](param: Param[T], value: T): StackingRegressionModel.this.type

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    Definition Classes
    Params

  71. final def setDefault(paramPairs: ParamPair[_]*): StackingRegressionModel.this.type

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    Attributes
    protected
    Definition Classes
    Params

  72. final def setDefault[T](param: Param[T], value: T): StackingRegressionModel.this.type

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    Attributes
    protected
    Definition Classes
    Params

  73. def setFeaturesCol(value: String): StackingRegressionModel

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    Definition Classes
    PredictionModel

  74. def setParent(parent: Estimator[StackingRegressionModel]): StackingRegressionModel

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    Definition Classes
    Model

  75. def setPredictionCol(value: String): StackingRegressionModel

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    Definition Classes
    PredictionModel

  76. val stack: EnsemblePredictionModelType

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  77. val stacker: Param[EnsemblePredictorType]

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    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

  78. final def synchronized[T0](arg0: ⇒ T0): T0

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    Definition Classes
    AnyRef

  79. def toString(): String

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    Definition Classes
    Identifiable → AnyRef → Any

  80. def transform(dataset: Dataset[_]): DataFrame

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    Definition Classes
    PredictionModel → Transformer

  81. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" )

  82. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

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    Definition Classes
    Transformer
    Annotations
    @Since( "2.0.0" ) @varargs()

  83. def transformImpl(dataset: Dataset[_]): DataFrame

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    Attributes
    protected
    Definition Classes
    PredictionModel

  84. def transformSchema(schema: StructType): StructType

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    Definition Classes
    PredictionModel → PipelineStage

  85. def transformSchema(schema: StructType, logging: Boolean): StructType

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    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()

  86. val uid: String

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    Definition Classes
    StackingRegressionModel → Identifiable

  87. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

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    Attributes
    protected
    Definition Classes
    PredictorParams

  88. final def wait(): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  89. final def wait(arg0: Long, arg1: Int): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  90. final def wait(arg0: Long): Unit

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    Definition Classes
    AnyRef
    Annotations
    @throws( ... )

  91. final val weightCol: Param[String]

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    Definition Classes
    HasWeightCol

  92. def write: MLWriter

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    Definition Classes
    StackingRegressionModel → MLWritable

Inherited from MLWritable

Inherited from StackingRegressorParams

Inherited from StackingParams

Inherited from HasBaseLearners

Inherited from HasStacker

Inherited from HasWeightCol

Inherited from HasParallelism

Inherited from PredictionModel[Vector, StackingRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[StackingRegressionModel]

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

getParam

param

Ungrouped