Packages

class BoostingRegressionModel extends RegressionModel[Vector, BoostingRegressionModel] with BoostingRegressorParams with MLWritable

Source
BoostingRegressor.scala
Linear Supertypes
MLWritable, BoostingRegressorParams, BoostingParams[EnsembleRegressorType], HasAggregationDepth, HasCheckpointInterval, HasBaseLearner[EnsembleRegressorType], HasWeightCol, HasNumBaseLearners, RegressionModel[Vector, BoostingRegressionModel], PredictionModel[Vector, BoostingRegressionModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[BoostingRegressionModel], Transformer, PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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  3. By Inheritance
Inherited
  1. BoostingRegressionModel
  2. MLWritable
  3. BoostingRegressorParams
  4. BoostingParams
  5. HasAggregationDepth
  6. HasCheckpointInterval
  7. HasBaseLearner
  8. HasWeightCol
  9. HasNumBaseLearners
  10. RegressionModel
  11. PredictionModel
  12. PredictorParams
  13. HasPredictionCol
  14. HasFeaturesCol
  15. HasLabelCol
  16. Model
  17. Transformer
  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 BoostingRegressionModel(weights: Array[Double], models: Array[EnsemblePredictionModelType])
  2. new BoostingRegressionModel(uid: String, weights: Array[Double], 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 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[_]): BoostingRegressionModel.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): BoostingRegressionModel
    Definition Classes
    BoostingRegressionModel → Model → Transformer → 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 explainParam(param: Param[_]): String
    Definition Classes
    Params
  17. def explainParams(): String
    Definition Classes
    Params
  18. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  19. def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  20. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  21. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  22. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  23. def featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  24. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  25. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  26. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  27. final def getAggregationDepth: Int
    Definition Classes
    HasAggregationDepth
  28. def getBaseLearner: EnsembleRegressorType

    Definition Classes
    HasBaseLearner
  29. final def getCheckpointInterval: Int
    Definition Classes
    HasCheckpointInterval
  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 getLossType: String

    Definition Classes
    BoostingRegressorParams
  35. def getNumBaseLearners: Int

    Definition Classes
    HasNumBaseLearners
  36. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  37. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  38. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  39. def getVotingStrategy: String

    Definition Classes
    BoostingRegressorParams
  40. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  41. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  42. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  43. def hasParent: Boolean
    Definition Classes
    Model
  44. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  45. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  46. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  47. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  48. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  49. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  50. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  51. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  52. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  53. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  54. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  55. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  56. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  57. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  58. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  59. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  60. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  61. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  62. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  63. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  64. 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
  65. val models: Array[EnsemblePredictionModelType]
  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. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since("1.6.0")
  71. val numModels: Int
  72. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  73. var parent: Estimator[BoostingRegressionModel]
    Definition Classes
    Model
  74. def predict(features: Vector): Double
    Definition Classes
    BoostingRegressionModel → PredictionModel
  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[_]): BoostingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  78. final def set(param: String, value: Any): BoostingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  79. final def set[T](param: Param[T], value: T): BoostingRegressionModel.this.type
    Definition Classes
    Params
  80. final def setDefault(paramPairs: ParamPair[_]*): BoostingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  81. final def setDefault[T](param: Param[T], value: T): BoostingRegressionModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  82. def setFeaturesCol(value: String): BoostingRegressionModel
    Definition Classes
    PredictionModel
  83. def setParent(parent: Estimator[BoostingRegressionModel]): BoostingRegressionModel
    Definition Classes
    Model
  84. def setPredictionCol(value: String): BoostingRegressionModel
    Definition Classes
    PredictionModel
  85. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  86. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  87. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    PredictionModel → Transformer
  88. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0")
  89. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0") @varargs()
  90. def transformImpl(dataset: Dataset[_]): DataFrame
    Attributes
    protected
    Definition Classes
    PredictionModel
  91. def transformSchema(schema: StructType): StructType
    Definition Classes
    PredictionModel → PipelineStage
  92. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  93. val uid: String
    Definition Classes
    BoostingRegressionModel → Identifiable
  94. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    PredictorParams
  95. 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
  96. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  97. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  98. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  99. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  100. val weights: Array[Double]
  101. def write: MLWriter
    Definition Classes
    BoostingRegressionModel → 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 RegressionModel[Vector, BoostingRegressionModel]

Inherited from PredictionModel[Vector, BoostingRegressionModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[BoostingRegressionModel]

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