Packages

class GBMRegressor extends Regressor[Vector, GBMRegressor, GBMRegressionModel] with GBMRegressorParams with MLWritable

Source
GBMRegressor.scala
Linear Supertypes
MLWritable, GBMRegressorParams, GBMParams, HasSubBag, HasSeed, BoostingParams[EnsembleRegressorType], HasAggregationDepth, HasCheckpointInterval, HasBaseLearner[EnsembleRegressorType], HasWeightCol, HasNumBaseLearners, HasValidationIndicatorCol, HasTol, HasMaxIter, Regressor[Vector, GBMRegressor, GBMRegressionModel], Predictor[Vector, GBMRegressor, GBMRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[GBMRegressionModel], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GBMRegressor
  2. MLWritable
  3. GBMRegressorParams
  4. GBMParams
  5. HasSubBag
  6. HasSeed
  7. BoostingParams
  8. HasAggregationDepth
  9. HasCheckpointInterval
  10. HasBaseLearner
  11. HasWeightCol
  12. HasNumBaseLearners
  13. HasValidationIndicatorCol
  14. HasTol
  15. HasMaxIter
  16. Regressor
  17. Predictor
  18. PredictorParams
  19. HasPredictionCol
  20. HasFeaturesCol
  21. HasLabelCol
  22. Estimator
  23. PipelineStage
  24. Logging
  25. Params
  26. Serializable
  27. Identifiable
  28. AnyRef
  29. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GBMRegressor()
  2. new GBMRegressor(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. val alpha: Param[Double]

    The alpha-quantile of the huber loss function and the quantile loss function.

    The alpha-quantile of the huber loss function and the quantile loss function. Only if loss="huber" or loss="quantile". (default = 0.9)

    Definition Classes
    GBMRegressorParams
  7. final def asInstanceOf[T0]: T0
    Definition Classes
    Any
  8. 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
  9. final val checkpointInterval: IntParam
    Definition Classes
    HasCheckpointInterval
  10. final def clear(param: Param[_]): GBMRegressor.this.type
    Definition Classes
    Params
  11. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.CloneNotSupportedException]) @native()
  12. def copy(extra: ParamMap): GBMRegressor
    Definition Classes
    GBMRegressor → Predictor → Estimator → PipelineStage → Params
  13. def copyValues[T <: Params](to: T, extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  14. final def defaultCopy[T <: Params](extra: ParamMap): T
    Attributes
    protected
    Definition Classes
    Params
  15. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  16. def equals(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef → Any
  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[_]): GBMRegressionModel
    Definition Classes
    Predictor → Estimator
  27. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GBMRegressionModel]
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  28. def fit(dataset: Dataset[_], paramMap: ParamMap): GBMRegressionModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  29. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBMRegressionModel
    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 getAlpha: Double

    Definition Classes
    GBMRegressorParams
  34. def getBaseLearner: EnsembleRegressorType

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

    Definition Classes
    GBMRegressorParams
  40. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  41. def getLearningRate: Double

    Definition Classes
    GBMParams
  42. def getLoss: String

    Definition Classes
    GBMRegressorParams
  43. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  44. def getNumBaseLearners: Int

    Definition Classes
    HasNumBaseLearners
  45. def getNumRounds: Int

    Definition Classes
    GBMParams
  46. def getOptimizedWeights: Boolean

    Definition Classes
    GBMParams
  47. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  48. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  49. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  50. def getReplacement: Boolean

    Definition Classes
    HasSubBag
  51. final def getSeed: Long
    Definition Classes
    HasSeed
  52. def getSubsampleRatio: Double

    Definition Classes
    HasSubBag
  53. def getSubspaceRatio: Double

    Definition Classes
    HasSubBag
  54. final def getTol: Double
    Definition Classes
    HasTol
  55. def getUpdates: String

    Definition Classes
    GBMParams
  56. final def getValidationIndicatorCol: String
    Definition Classes
    HasValidationIndicatorCol
  57. final def getValidationTol: Double

    Definition Classes
    GBMParams
  58. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  59. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  60. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  61. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  62. val initStrategy: Param[String]

    strategy for the init predictions, can be a constant optimized for the minimized loss, zero, or the base learner learned on labels.

    strategy for the init predictions, can be a constant optimized for the minimized loss, zero, or the base learner learned on labels. (case-insensitive) Supported: "constant", "zero", "base". (default = constant)

    Definition Classes
    GBMRegressorParams
  63. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  64. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  65. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  66. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  67. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  68. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  70. val learningRate: Param[Double]

    param for the learning rate of the algorithm

    param for the learning rate of the algorithm

    Definition Classes
    GBMParams
  71. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  72. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  79. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. val loss: Param[String]

    Loss function which GBM tries to minimize.

    Loss function which GBM tries to minimize. (case-insensitive) Supported: "squared", "absolute", "huber", "quantile". (default = squared)

    Definition Classes
    GBMRegressorParams
  84. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  85. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  86. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  87. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  88. 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
  89. val numRounds: Param[Int]

    param for the number of round waiting for next decrease in validation set

    param for the number of round waiting for next decrease in validation set

    Definition Classes
    GBMParams
  90. val optimizedWeights: Param[Boolean]

    param for using optimized weights in GBM

    param for using optimized weights in GBM

    Definition Classes
    GBMParams
  91. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  92. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  93. val replacement: Param[Boolean]

    param for whether samples are drawn with replacement

    param for whether samples are drawn with replacement

    Definition Classes
    HasSubBag
  94. 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.")
  95. final val seed: LongParam
    Definition Classes
    HasSeed
  96. final def set(paramPair: ParamPair[_]): GBMRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  97. final def set(param: String, value: Any): GBMRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  98. final def set[T](param: Param[T], value: T): GBMRegressor.this.type
    Definition Classes
    Params
  99. def setAggregationDepth(value: Int): GBMRegressor.this.type

  100. def setAlpha(value: Double): GBMRegressor.this.type

  101. def setBaseLearner(value: EnsembleRegressorType): GBMRegressor.this.type
  102. def setCheckpointInterval(value: Int): GBMRegressor.this.type

  103. final def setDefault(paramPairs: ParamPair[_]*): GBMRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  104. final def setDefault[T](param: Param[T], value: T): GBMRegressor.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. def setFeaturesCol(value: String): GBMRegressor
    Definition Classes
    Predictor
  106. def setInitStrategy(value: String): GBMRegressor.this.type

  107. def setLabelCol(value: String): GBMRegressor
    Definition Classes
    Predictor
  108. def setLearningRate(value: Double): GBMRegressor.this.type

  109. def setLoss(value: String): GBMRegressor.this.type

  110. def setMaxIter(value: Int): GBMRegressor.this.type

  111. def setNumBaseLearners(value: Int): GBMRegressor.this.type

  112. def setNumRounds(value: Int): GBMRegressor.this.type

  113. def setOptimizedWeights(value: Boolean): GBMRegressor.this.type

  114. def setPredictionCol(value: String): GBMRegressor
    Definition Classes
    Predictor
  115. def setReplacement(value: Boolean): GBMRegressor.this.type

  116. def setSeed(value: Long): GBMRegressor.this.type

  117. def setSubsampleRatio(value: Double): GBMRegressor.this.type

  118. def setSubspaceRatio(value: Double): GBMRegressor.this.type

  119. def setTol(value: Double): GBMRegressor.this.type

  120. def setUpdates(value: String): GBMRegressor.this.type

  121. def setValidationIndicatorCol(value: String): GBMRegressor.this.type

  122. def setValidationTol(value: Double): GBMRegressor.this.type

  123. def setWeightCol(value: String): GBMRegressor.this.type

  124. def slice(indices: Array[Int]): (Vector) => Vector
    Attributes
    protected
    Definition Classes
    HasSubBag
  125. 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
  126. def subspace(subspaceRatio: Double, numFeatures: Int, seed: Long): Array[Int]
    Attributes
    protected
    Definition Classes
    HasSubBag
  127. 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
  128. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  129. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  130. final val tol: DoubleParam
    Definition Classes
    HasTol
  131. def train(dataset: Dataset[_]): GBMRegressionModel
    Attributes
    protected
    Definition Classes
    GBMRegressor → Predictor
  132. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  133. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  134. val uid: String
    Definition Classes
    GBMRegressor → Identifiable
  135. val updates: Param[String]

    Newton (using hessian) or Gradient updates.

    Newton (using hessian) or Gradient updates. (case-insensitive) Supported: "gradient", "newton". (default = gradient)

    Definition Classes
    GBMParams
  136. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  137. final val validationIndicatorCol: Param[String]
    Definition Classes
    HasValidationIndicatorCol
  138. final val validationTol: DoubleParam

    Threshold for stopping early when fit with validation is used.

    Threshold for stopping early when fit with validation is used. (This parameter is ignored when fit without validation is used.) The decision to stop early is decided based on this logic: If the current loss on the validation set is greater than 0.01, the diff of validation error is compared to relative tolerance which is validationTol * (current loss on the validation set). If the current loss on the validation set is less than or equal to 0.01, the diff of validation error is compared to absolute tolerance which is validationTol * 0.01.

    Definition Classes
    GBMParams
    See also

    validationIndicatorCol

  139. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  140. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  141. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  142. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  143. def write: MLWriter
    Definition Classes
    GBMRegressor → MLWritable

Inherited from MLWritable

Inherited from GBMRegressorParams

Inherited from GBMParams

Inherited from HasSubBag

Inherited from HasSeed

Inherited from BoostingParams[EnsembleRegressorType]

Inherited from HasAggregationDepth

Inherited from HasCheckpointInterval

Inherited from HasBaseLearner[EnsembleRegressorType]

Inherited from HasWeightCol

Inherited from HasNumBaseLearners

Inherited from HasValidationIndicatorCol

Inherited from HasTol

Inherited from HasMaxIter

Inherited from Regressor[Vector, GBMRegressor, GBMRegressionModel]

Inherited from Predictor[Vector, GBMRegressor, GBMRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[GBMRegressionModel]

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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