Packages

class BaggingRegressionModel extends RegressionModel[Vector, BaggingRegressionModel] with BaggingRegressorParams with MLWritable

Source
BaggingRegressor.scala
Linear Supertypes
MLWritable, BaggingRegressorParams, BaggingParams[EnsembleRegressorType], HasSubBag, HasSeed, HasBaseLearner[EnsembleRegressorType], HasWeightCol, HasParallelism, HasNumBaseLearners, RegressionModel[Vector, BaggingRegressionModel], PredictionModel[Vector, BaggingRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[BaggingRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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  2. Alphabetic
  3. By Inheritance
Inherited
  1. BaggingRegressionModel
  2. MLWritable
  3. BaggingRegressorParams
  4. BaggingParams
  5. HasSubBag
  6. HasSeed
  7. HasBaseLearner
  8. HasWeightCol
  9. HasParallelism
  10. HasNumBaseLearners
  11. RegressionModel
  12. PredictionModel
  13. PredictorParams
  14. HasPredictionCol
  15. HasFeaturesCol
  16. HasLabelCol
  17. Model
  18. Transformer
  19. PipelineStage
  20. Logging
  21. Params
  22. Serializable
  23. Identifiable
  24. AnyRef
  25. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new BaggingRegressionModel(subspaces: Array[Array[Int]], models: Array[EnsemblePredictionModelType])
  2. new BaggingRegressionModel(uid: String, subspaces: Array[Array[Int]], models: Array[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. final def clear(param: Param[_]): BaggingRegressionModel.this.type
    Definition Classes
    Params
  8. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  9. def copy(extra: ParamMap): BaggingRegressionModel
    Definition Classes
    BaggingRegressionModel → Model → Transformer → PipelineStage → Params
  10. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  11. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  12. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  13. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  14. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  15. def explainParams(): String
    Definition Classes
    Params
  16. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  17. def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  18. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  19. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  20. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  21. def featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  22. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  23. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  24. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  25. def getBaseLearner: EnsembleRegressorType

    Definition Classes
    HasBaseLearner
  26. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  27. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  29. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  30. def getNumBaseLearners: Int

    Definition Classes
    HasNumBaseLearners
  31. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  32. def getParallelism: Int
    Definition Classes
    HasParallelism
  33. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  34. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  35. def getReplacement: Boolean

    Definition Classes
    HasSubBag
  36. final def getSeed: Long
    Definition Classes
    HasSeed
  37. def getSubsampleRatio: Double

    Definition Classes
    HasSubBag
  38. def getSubspaceRatio: Double

    Definition Classes
    HasSubBag
  39. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  40. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  41. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  42. def hasParent: Boolean
    Definition Classes
    Model
  43. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  44. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  45. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  46. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  47. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  48. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  49. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  51. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  52. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  59. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. val models: Array[EnsemblePredictionModelType]
  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 numBaseLearners: Param[Int]

    param for the number of base learners of the algorithm

    param for the number of base learners of the algorithm

    Definition Classes
    HasNumBaseLearners
  68. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since("1.6.0")
  69. val numModels: Int
  70. val parallelism: IntParam
    Definition Classes
    HasParallelism
  71. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  72. var parent: Estimator[BaggingRegressionModel]
    Definition Classes
    Model
  73. def predict(features: Vector): Double
    Definition Classes
    BaggingRegressionModel → PredictionModel
  74. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  75. val replacement: Param[Boolean]

    param for whether samples are drawn with replacement

    param for whether samples are drawn with replacement

    Definition Classes
    HasSubBag
  76. 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.")
  77. final val seed: LongParam
    Definition Classes
    HasSeed
  78. final def set(paramPair: ParamPair[_]): BaggingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  79. final def set(param: String, value: Any): BaggingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  80. final def set[T](param: Param[T], value: T): BaggingRegressionModel.this.type
    Definition Classes
    Params
  81. final def setDefault(paramPairs: ParamPair[_]*): BaggingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  82. final def setDefault[T](param: Param[T], value: T): BaggingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  83. def setFeaturesCol(value: String): BaggingRegressionModel
    Definition Classes
    PredictionModel
  84. def setParent(parent: Estimator[BaggingRegressionModel]): BaggingRegressionModel
    Definition Classes
    Model
  85. def setPredictionCol(value: String): BaggingRegressionModel
    Definition Classes
    PredictionModel
  86. def slice(indices: Array[Int]): (Vector) => Vector
    Attributes
    protected
    Definition Classes
    HasSubBag
  87. val subsampleRatio: Param[Double]

    param for ratio of rows sampled out of the dataset

    param for ratio of rows sampled out of the dataset

    Definition Classes
    HasSubBag
  88. def subspace(subspaceRatio: Double, numFeatures: Int, seed: Long): Array[Int]
    Attributes
    protected
    Definition Classes
    HasSubBag
  89. val subspaceRatio: Param[Double]

    param for ratio of rows sampled out of the dataset

    param for ratio of rows sampled out of the dataset

    Definition Classes
    HasSubBag
  90. val subspaces: Array[Array[Int]]
  91. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  92. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  93. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    PredictionModel → Transformer
  94. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0")
  95. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0") @varargs()
  96. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  97. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  98. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  99. val uid: String
    Definition Classes
    BaggingRegressionModel → Identifiable
  100. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  101. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  102. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  103. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  104. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  105. def write: MLWriter
    Definition Classes
    BaggingRegressionModel → MLWritable

Inherited from MLWritable

Inherited from BaggingRegressorParams

Inherited from BaggingParams[EnsembleRegressorType]

Inherited from HasSubBag

Inherited from HasSeed

Inherited from HasBaseLearner[EnsembleRegressorType]

Inherited from HasWeightCol

Inherited from HasParallelism

Inherited from HasNumBaseLearners

Inherited from RegressionModel[Vector, BaggingRegressionModel]

Inherited from PredictionModel[Vector, BaggingRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[BaggingRegressionModel]

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