Packages

class BaggingRegressor extends Regressor[Vector, BaggingRegressor, BaggingRegressionModel] with BaggingRegressorParams with MLWritable

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

Instance Constructors

  1. new BaggingRegressor()
  2. new BaggingRegressor(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. final def clear(param: Param[_]): BaggingRegressor.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): BaggingRegressor
    Definition Classes
    BaggingRegressor → Predictor → Estimator → 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. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
    Attributes
    protected
    Definition Classes
    Predictor
  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 finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  23. def fit(dataset: Dataset[_]): BaggingRegressionModel
    Definition Classes
    Predictor → Estimator
  24. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[BaggingRegressionModel]
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  25. def fit(dataset: Dataset[_], paramMap: ParamMap): BaggingRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  26. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BaggingRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0") @varargs()
  27. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  28. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  29. def getBaseLearner: EnsembleRegressorType

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

    Definition Classes
    HasNumBaseLearners
  35. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  36. def getParallelism: Int
    Definition Classes
    HasParallelism
  37. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  38. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  39. def getReplacement: Boolean

    Definition Classes
    HasSubBag
  40. final def getSeed: Long
    Definition Classes
    HasSeed
  41. def getSubsampleRatio: Double

    Definition Classes
    HasSubBag
  42. def getSubspaceRatio: Double

    Definition Classes
    HasSubBag
  43. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  44. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  45. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  46. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  47. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  48. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  49. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  50. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  51. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  52. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  53. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  54. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  55. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  62. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  67. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  68. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  69. 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
  70. val parallelism: IntParam
    Definition Classes
    HasParallelism
  71. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  72. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  73. val replacement: Param[Boolean]

    param for whether samples are drawn with replacement

    param for whether samples are drawn with replacement

    Definition Classes
    HasSubBag
  74. 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.")
  75. final val seed: LongParam
    Definition Classes
    HasSeed
  76. final def set(paramPair: ParamPair[_]): BaggingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  77. final def set(param: String, value: Any): BaggingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set[T](param: Param[T], value: T): BaggingRegressor.this.type
    Definition Classes
    Params
  79. def setBaseLearner(value: EnsembleRegressorType): BaggingRegressor.this.type

  80. final def setDefault(paramPairs: ParamPair[_]*): BaggingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  81. final def setDefault[T](param: Param[T], value: T): BaggingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  82. def setFeaturesCol(value: String): BaggingRegressor
    Definition Classes
    Predictor
  83. def setLabelCol(value: String): BaggingRegressor
    Definition Classes
    Predictor
  84. def setNumBaseLearners(value: Int): BaggingRegressor.this.type

  85. def setParallelism(value: Int): BaggingRegressor.this.type

    Set the maximum level of parallelism to evaluate models in parallel.

    Set the maximum level of parallelism to evaluate models in parallel. Default is 1 for serial evaluation

  86. def setPredictionCol(value: String): BaggingRegressor
    Definition Classes
    Predictor
  87. def setReplacement(value: Boolean): BaggingRegressor.this.type

  88. def setSubsampleRatio(value: Double): BaggingRegressor.this.type

  89. def setSubspaceRatio(value: Double): BaggingRegressor.this.type

  90. def setWeightCol(value: String): BaggingRegressor.this.type

  91. def slice(indices: Array[Int]): (Vector) => Vector
    Attributes
    protected
    Definition Classes
    HasSubBag
  92. 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
  93. def subspace(subspaceRatio: Double, numFeatures: Int, seed: Long): Array[Int]
    Attributes
    protected
    Definition Classes
    HasSubBag
  94. 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
  95. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  96. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  97. def train(dataset: Dataset[_]): BaggingRegressionModel
    Attributes
    protected
    Definition Classes
    BaggingRegressor → Predictor
  98. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  99. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  100. val uid: String
    Definition Classes
    BaggingRegressor → Identifiable
  101. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  102. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  103. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  104. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  105. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  106. def write: MLWriter
    Definition Classes
    BaggingRegressor → 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 Regressor[Vector, BaggingRegressor, BaggingRegressionModel]

Inherited from Predictor[Vector, BaggingRegressor, BaggingRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[BaggingRegressionModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

expertSetParam

getParam

param

setParam

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