Packages

class GBMRegressionModel extends RegressionModel[Vector, 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, RegressionModel[Vector, GBMRegressionModel], PredictionModel[Vector, GBMRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[GBMRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GBMRegressionModel
  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. RegressionModel
  17. PredictionModel
  18. PredictorParams
  19. HasPredictionCol
  20. HasFeaturesCol
  21. HasLabelCol
  22. Model
  23. Transformer
  24. PipelineStage
  25. Logging
  26. Params
  27. Serializable
  28. Identifiable
  29. AnyRef
  30. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GBMRegressionModel(weights: Array[Double], subspaces: Array[Array[Int]], models: Array[EnsemblePredictionModelType], init: EnsemblePredictionModelType)
  2. new GBMRegressionModel(uid: String, weights: Array[Double], subspaces: Array[Array[Int]], models: Array[EnsemblePredictionModelType], init: 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 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[_]): GBMRegressionModel.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): GBMRegressionModel
    Definition Classes
    GBMRegressionModel → Model → Transformer → 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. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  22. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  23. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  24. def featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  25. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  26. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  27. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  28. final def getAggregationDepth: Int
    Definition Classes
    HasAggregationDepth
  29. def getAlpha: Double

    Definition Classes
    GBMRegressorParams
  30. def getBaseLearner: EnsembleRegressorType

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

    Definition Classes
    GBMRegressorParams
  36. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  37. def getLearningRate: Double

    Definition Classes
    GBMParams
  38. def getLoss: String

    Definition Classes
    GBMRegressorParams
  39. final def getMaxIter: Int
    Definition Classes
    HasMaxIter
  40. def getNumBaseLearners: Int

    Definition Classes
    HasNumBaseLearners
  41. def getNumRounds: Int

    Definition Classes
    GBMParams
  42. def getOptimizedWeights: Boolean

    Definition Classes
    GBMParams
  43. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  44. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  45. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  46. def getReplacement: Boolean

    Definition Classes
    HasSubBag
  47. final def getSeed: Long
    Definition Classes
    HasSeed
  48. def getSubsampleRatio: Double

    Definition Classes
    HasSubBag
  49. def getSubspaceRatio: Double

    Definition Classes
    HasSubBag
  50. final def getTol: Double
    Definition Classes
    HasTol
  51. def getUpdates: String

    Definition Classes
    GBMParams
  52. final def getValidationIndicatorCol: String
    Definition Classes
    HasValidationIndicatorCol
  53. final def getValidationTol: Double

    Definition Classes
    GBMParams
  54. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  55. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  56. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  57. def hasParent: Boolean
    Definition Classes
    Model
  58. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  59. val init: EnsemblePredictionModelType
  60. 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
  61. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  62. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  64. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  65. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  66. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  68. val learningRate: Param[Double]

    param for the learning rate of the algorithm

    param for the learning rate of the algorithm

    Definition Classes
    GBMParams
  69. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  70. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  71. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  72. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  73. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  74. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  75. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  77. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. 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
  82. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  83. val models: Array[EnsemblePredictionModelType]
  84. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  85. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  86. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  87. 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
  88. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since("1.6.0")
  89. val numModels: Int
  90. 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
  91. val optimizedWeights: Param[Boolean]

    param for using optimized weights in GBM

    param for using optimized weights in GBM

    Definition Classes
    GBMParams
  92. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  93. var parent: Estimator[GBMRegressionModel]
    Definition Classes
    Model
  94. def predict(features: Vector): Double
    Definition Classes
    GBMRegressionModel → PredictionModel
  95. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  96. val replacement: Param[Boolean]

    param for whether samples are drawn with replacement

    param for whether samples are drawn with replacement

    Definition Classes
    HasSubBag
  97. 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.")
  98. final val seed: LongParam
    Definition Classes
    HasSeed
  99. final def set(paramPair: ParamPair[_]): GBMRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  100. final def set(param: String, value: Any): GBMRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  101. final def set[T](param: Param[T], value: T): GBMRegressionModel.this.type
    Definition Classes
    Params
  102. final def setDefault(paramPairs: ParamPair[_]*): GBMRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  103. final def setDefault[T](param: Param[T], value: T): GBMRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  104. def setFeaturesCol(value: String): GBMRegressionModel
    Definition Classes
    PredictionModel
  105. def setParent(parent: Estimator[GBMRegressionModel]): GBMRegressionModel
    Definition Classes
    Model
  106. def setPredictionCol(value: String): GBMRegressionModel
    Definition Classes
    PredictionModel
  107. def slice(indices: Array[Int]): (Vector) => Vector
    Attributes
    protected
    Definition Classes
    HasSubBag
  108. 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
  109. def subspace(subspaceRatio: Double, numFeatures: Int, seed: Long): Array[Int]
    Attributes
    protected
    Definition Classes
    HasSubBag
  110. 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
  111. val subspaces: Array[Array[Int]]
  112. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  113. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  114. final val tol: DoubleParam
    Definition Classes
    HasTol
  115. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    PredictionModel → Transformer
  116. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0")
  117. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0") @varargs()
  118. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  119. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  120. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  121. val uid: String
    Definition Classes
    GBMRegressionModel → Identifiable
  122. 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
  123. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  124. final val validationIndicatorCol: Param[String]
    Definition Classes
    HasValidationIndicatorCol
  125. 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

  126. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  127. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  128. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  129. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  130. val weights: Array[Double]
  131. def write: MLWriter
    Definition Classes
    GBMRegressionModel → 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 RegressionModel[Vector, GBMRegressionModel]

Inherited from PredictionModel[Vector, GBMRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GBMRegressionModel]

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