Packages

class BoostingRegressor extends Regressor[Vector, BoostingRegressor, BoostingRegressionModel] with BoostingRegressorParams with MLWritable

Source
BoostingRegressor.scala
Linear Supertypes
MLWritable, BoostingRegressorParams, BoostingParams[EnsembleRegressorType], HasAggregationDepth, HasCheckpointInterval, HasBaseLearner[EnsembleRegressorType], HasWeightCol, HasNumBaseLearners, Regressor[Vector, BoostingRegressor, BoostingRegressionModel], Predictor[Vector, BoostingRegressor, BoostingRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[BoostingRegressionModel], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. BoostingRegressor
  2. MLWritable
  3. BoostingRegressorParams
  4. BoostingParams
  5. HasAggregationDepth
  6. HasCheckpointInterval
  7. HasBaseLearner
  8. HasWeightCol
  9. HasNumBaseLearners
  10. Regressor
  11. Predictor
  12. PredictorParams
  13. HasPredictionCol
  14. HasFeaturesCol
  15. HasLabelCol
  16. Estimator
  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 BoostingRegressor()
  2. new BoostingRegressor(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 val aggregationDepth: IntParam
    Definition Classes
    HasAggregationDepth
  6. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  7. 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
  8. final val checkpointInterval: IntParam
    Definition Classes
    HasCheckpointInterval
  9. final def clear(param: Param[_]): BoostingRegressor.this.type
    Definition Classes
    Params
  10. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  11. def copy(extra: ParamMap): BoostingRegressor
    Definition Classes
    BoostingRegressor → Predictor → Estimator → PipelineStage → Params
  12. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  13. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  14. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  15. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  16. def error(label: Double, prediction: Double): Double
  17. def explainParam(param: Param[_]): String
    Definition Classes
    Params
  18. def explainParams(): String
    Definition Classes
    Params
  19. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  20. def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  21. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
    Attributes
    protected
    Definition Classes
    Predictor
  22. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  24. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  25. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  26. def fit(dataset: Dataset[_]): BoostingRegressionModel
    Definition Classes
    Predictor → Estimator
  27. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[BoostingRegressionModel]
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  28. def fit(dataset: Dataset[_], paramMap: ParamMap): BoostingRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  29. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): BoostingRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0") @varargs()
  30. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  31. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  32. final def getAggregationDepth: Int
    Definition Classes
    HasAggregationDepth
  33. def getBaseLearner: EnsembleRegressorType

    Definition Classes
    HasBaseLearner
  34. final def getCheckpointInterval: Int
    Definition Classes
    HasCheckpointInterval
  35. final def getClass(): Class[_ <: AnyRef]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  36. final def getDefault[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  37. final def getFeaturesCol: String
    Definition Classes
    HasFeaturesCol
  38. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  39. def getLossType: String

    Definition Classes
    BoostingRegressorParams
  40. def getNumBaseLearners: Int

    Definition Classes
    HasNumBaseLearners
  41. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  42. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  43. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  44. def getVotingStrategy: String

    Definition Classes
    BoostingRegressorParams
  45. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  46. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  47. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  48. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  49. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  50. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  51. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  52. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  53. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  54. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  55. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  56. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  57. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  60. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  64. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  66. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  67. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. def loss(error: Double): Double
  69. val lossType: Param[String]

    Loss function which Boosting tries to minimize.

    Loss function which Boosting tries to minimize. (case-insensitive) Supported: "exponential", "linear", "squared". (default = exponential)

    Definition Classes
    BoostingRegressorParams
  70. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  71. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  72. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  73. 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
  74. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  75. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  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 def set(paramPair: ParamPair[_]): BoostingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set(param: String, value: Any): BoostingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  79. final def set[T](param: Param[T], value: T): BoostingRegressor.this.type
    Definition Classes
    Params
  80. def setBaseLearner(value: EnsembleRegressorType): BoostingRegressor.this.type
  81. def setCheckpointInterval(value: Int): BoostingRegressor.this.type

  82. final def setDefault(paramPairs: ParamPair[_]*): BoostingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  83. final def setDefault[T](param: Param[T], value: T): BoostingRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  84. def setFeaturesCol(value: String): BoostingRegressor
    Definition Classes
    Predictor
  85. def setLabelCol(value: String): BoostingRegressor
    Definition Classes
    Predictor
  86. def setLossType(value: String): BoostingRegressor.this.type

  87. def setNumBaseLearners(value: Int): BoostingRegressor.this.type

  88. def setPredictionCol(value: String): BoostingRegressor
    Definition Classes
    Predictor
  89. def setVotingStrategy(value: String): BoostingRegressor.this.type

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

  91. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  92. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  93. def train(dataset: Dataset[_]): BoostingRegressionModel
    Attributes
    protected
    Definition Classes
    BoostingRegressor → Predictor
  94. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  95. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  96. val uid: String
    Definition Classes
    BoostingRegressor → Identifiable
  97. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  98. val votingStrategy: Param[String]

    Voting strategy to aggregate predictions of base regressor.

    Voting strategy to aggregate predictions of base regressor. (case-insensitive) Supported: "median", "mean". (default = median)

    Definition Classes
    BoostingRegressorParams
  99. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  100. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  101. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  102. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  103. def write: MLWriter
    Definition Classes
    BoostingRegressor → MLWritable

Inherited from MLWritable

Inherited from BoostingRegressorParams

Inherited from BoostingParams[EnsembleRegressorType]

Inherited from HasAggregationDepth

Inherited from HasCheckpointInterval

Inherited from HasBaseLearner[EnsembleRegressorType]

Inherited from HasWeightCol

Inherited from HasNumBaseLearners

Inherited from Regressor[Vector, BoostingRegressor, BoostingRegressionModel]

Inherited from Predictor[Vector, BoostingRegressor, BoostingRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[BoostingRegressionModel]

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

getParam

param

setParam

Ungrouped