Packages

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

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

Instance Constructors

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

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[_]): StackingRegressionModel.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): StackingRegressionModel
    Definition Classes
    StackingRegressionModel → Model → Transformer → 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. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  20. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  21. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  22. def featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  23. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  24. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  25. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  26. def getBaseLearner: EnsembleRegressorType

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

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

    Definition Classes
    HasStacker
  37. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  38. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  39. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  40. def hasParent: Boolean
    Definition Classes
    Model
  41. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  42. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  43. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  44. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  45. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  46. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  47. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  48. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  49. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  50. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  52. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  57. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. val models: Array[EnsemblePredictionModelType]
  62. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  63. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  64. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  65. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since("1.6.0")
  66. val parallelism: IntParam
    Definition Classes
    HasParallelism
  67. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  68. var parent: Estimator[StackingRegressionModel]
    Definition Classes
    Model
  69. def predict(features: Vector): Double
    Definition Classes
    StackingRegressionModel → PredictionModel
  70. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  71. 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.")
  72. final def set(paramPair: ParamPair[_]): StackingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  73. final def set(param: String, value: Any): StackingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  74. final def set[T](param: Param[T], value: T): StackingRegressionModel.this.type
    Definition Classes
    Params
  75. final def setDefault(paramPairs: ParamPair[_]*): StackingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  76. final def setDefault[T](param: Param[T], value: T): StackingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  77. def setFeaturesCol(value: String): StackingRegressionModel
    Definition Classes
    PredictionModel
  78. def setParent(parent: Estimator[StackingRegressionModel]): StackingRegressionModel
    Definition Classes
    Model
  79. def setPredictionCol(value: String): StackingRegressionModel
    Definition Classes
    PredictionModel
  80. val stack: EnsemblePredictionModelType
  81. 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
  82. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  83. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  84. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    PredictionModel → Transformer
  85. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0")
  86. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0") @varargs()
  87. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  88. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  89. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  90. val uid: String
    Definition Classes
    StackingRegressionModel → Identifiable
  91. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  92. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  93. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  94. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  95. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  96. def write: MLWriter
    Definition Classes
    StackingRegressionModel → 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 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 Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

Ungrouped