BoostingRegressionModel

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. def avgLoss(lossColName: String, boostProbaColName: String)(df: DataFrame): Double

   

   Definition Classes

   BoostingParams

7. val baseLearner: Param[EnsemblePredictorType]

   

   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. def beta(avgl: Double, numClasses: Int = 2): Double

   

   Definition Classes

   BoostingParams

9. final def clear(param: Param[_]): BoostingRegressionModel.this.type

   

   Definition Classes

   Params

10. def clone(): AnyRef

    

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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: Any): Boolean

    

    Definition Classes

    AnyRef → Any

16. def evaluateOnValidation(numClasses: Int, weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

    

    Definition Classes

    BoostingParams

17. def evaluateOnValidation(weights: Array[Double], boosters: Array[EnsemblePredictionModelType], labelColName: String, featuresColName: String, loss: (Double) ⇒ Double)(df: DataFrame): Double

    

    Definition Classes

    BoostingParams

18. def explainParam(param: Param[_]): String

    

    Definition Classes

    Params

19. def explainParams(): String

    

    Definition Classes

    Params

20. def extractBoostedBag(poissonProbaColName: String, seed: Long)(df: DataFrame): DataFrame

    

    Definition Classes

    BoostingParams

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[java.lang]

    Definition Classes

    AnyRef

    Annotations

    @throws( classOf[java.lang.Throwable] )

26. def fitBaseLearner(baseLearner: EnsemblePredictorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType

    

    Definition Classes

    HasBaseLearner

27. final def get[T](param: Param[T]): Option[T]

    

    Definition Classes

    Params

28. def getBaseLearner: EnsemblePredictorType

    
    Definition Classes

    HasBaseLearner

29. final def getClass(): Class[_]

    

    Definition Classes

    AnyRef → Any

30. final def getDefault[T](param: Param[T]): Option[T]

    

    Definition Classes

    Params

31. final def getFeaturesCol: String

    

    Definition Classes

    HasFeaturesCol

32. final def getLabelCol: String

    

    Definition Classes

    HasLabelCol

33. def getLoss: String

    
    Definition Classes

    BoostingRegressorParams

34. def getNumBaseLearners: Int

    
    Definition Classes

    HasNumBaseLearners

35. def getNumRound: Int

    
    Definition Classes

    HasNumRound

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. final def getSeed: Long

    

    Definition Classes

    HasSeed

40. final def getTol: Double

    

    Definition Classes

    HasTol

41. final def getValidationIndicatorCol: String

    

    Definition Classes

    HasValidationIndicatorCol

42. final def getWeightCol: String

    

    Definition Classes

    HasWeightCol

43. final def hasDefault[T](param: Param[T]): Boolean

    

    Definition Classes

    Params

44. def hasParam(paramName: String): Boolean

    

    Definition Classes

    Params

45. def hasParent: Boolean

    

    Definition Classes

    Model

46. def hashCode(): Int

    

    Definition Classes

    AnyRef → Any

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

    

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    protected

    Definition Classes

    Logging

57. def logError(msg: ⇒ String, throwable: Throwable): Unit

    

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    protected

    Definition Classes

    Logging

58. def logError(msg: ⇒ String): Unit

    

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    protected

    Definition Classes

    Logging

59. def logInfo(msg: ⇒ String, throwable: Throwable): Unit

    

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    protected

    Definition Classes

    Logging

60. def logInfo(msg: ⇒ String): Unit

    

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    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. val loss: Param[String]

    

    Loss function which Boosting tries to minimize.

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

    Definition Classes

    BoostingRegressorParams

67. val models: Array[EnsemblePredictionModelType]

    

68. final def ne(arg0: AnyRef): Boolean

    

    Definition Classes

    AnyRef

69. final def notify(): Unit

    

    Definition Classes

    AnyRef

70. final def notifyAll(): Unit

    

    Definition Classes

    AnyRef

71. 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

72. val numBaseModels: Int

    

73. def numFeatures: Int

    

    Definition Classes

    PredictionModel

    Annotations

    @Since( "1.6.0" )

74. val numRound: 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

    HasNumRound

75. lazy val params: Array[Param[_]]

    

    Definition Classes

    Params

76. var parent: Estimator[BoostingRegressionModel]

    

    Definition Classes

    Model

77. def predict(features: Vector): Double

    

    Definition Classes

    BoostingRegressionModel → PredictionModel

78. final val predictionCol: Param[String]

    

    Definition Classes

    HasPredictionCol

79. def probabilize(boostWeightColName: String, boostProbaColName: String, poissonProbaColName: String)(df: DataFrame): DataFrame

    

    Definition Classes

    BoostingParams

80. def save(path: String): Unit

    

    Definition Classes

    MLWritable

    Annotations

    @Since( "1.6.0" ) @throws( ... )

81. final val seed: LongParam

    

    Definition Classes

    HasSeed

82. final def set(paramPair: ParamPair[_]): BoostingRegressionModel.this.type

    

    Attributes

    protected

    Definition Classes

    Params

83. final def set(param: String, value: Any): BoostingRegressionModel.this.type

    

    Attributes

    protected

    Definition Classes

    Params

84. final def set[T](param: Param[T], value: T): BoostingRegressionModel.this.type

    

    Definition Classes

    Params

85. final def setDefault(paramPairs: ParamPair[_]*): BoostingRegressionModel.this.type

    

    Attributes

    protected

    Definition Classes

    Params

86. final def setDefault[T](param: Param[T], value: T): BoostingRegressionModel.this.type

    

    Attributes

    protected

    Definition Classes

    Params

87. def setFeaturesCol(value: String): BoostingRegressionModel

    

    Definition Classes

    PredictionModel

88. def setParent(parent: Estimator[BoostingRegressionModel]): BoostingRegressionModel

    

    Definition Classes

    Model

89. def setPredictionCol(value: String): BoostingRegressionModel

    

    Definition Classes

    PredictionModel

90. final def synchronized[T0](arg0: ⇒ T0): T0

    

    Definition Classes

    AnyRef

91. def terminate(avgl: Double, withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation, numClasses: Double = 2.0): (Int, Double, Int)

    

    Definition Classes

    BoostingParams

92. def terminateVal(withValidation: Boolean, error: Double, verror: Double, tol: Double, numRound: Int, numTry: Int, iter: Int, instrumentation: Instrumentation): (Int, Double, Int)

    

    Definition Classes

    BoostingParams

93. def toString(): String

    

    Definition Classes

    Identifiable → AnyRef → Any

94. final val tol: DoubleParam

    

    Definition Classes

    HasTol

95. def transform(dataset: Dataset[_]): DataFrame

    

    Definition Classes

    PredictionModel → Transformer

96. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame

    

    Definition Classes

    Transformer

    Annotations

    @Since( "2.0.0" )

97. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame

    

    Definition Classes

    Transformer

    Annotations

    @Since( "2.0.0" ) @varargs()

98. def transformImpl(dataset: Dataset[_]): DataFrame

    

    Attributes

    protected

    Definition Classes

    PredictionModel

99. def transformSchema(schema: StructType): StructType

    

    Definition Classes

    PredictionModel → PipelineStage

100. def transformSchema(schema: StructType, logging: Boolean): StructType

     

     Attributes

     protected

     Definition Classes

     PipelineStage

     Annotations

     @DeveloperApi()

101. val uid: String

     

     Definition Classes

     BoostingRegressionModel → Identifiable

102. def updateWeights(boostWeightColName: String, lossColName: String, beta: Double, updatedBoostWeightColName: String)(df: DataFrame): DataFrame

     

     Definition Classes

     BoostingParams

103. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

     

     Attributes

     protected

     Definition Classes

     PredictorParams

104. final val validationIndicatorCol: Param[String]

     

     Definition Classes

     HasValidationIndicatorCol

105. final def wait(): Unit

     

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

106. final def wait(arg0: Long, arg1: Int): Unit

     

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

107. final def wait(arg0: Long): Unit

     

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

108. def weight(beta: Double): Double

     

     Definition Classes

     BoostingParams

109. final val weightCol: Param[String]

     

     Definition Classes

     HasWeightCol

110. val weights: Array[Double]

     

111. def write: MLWriter

     

     Definition Classes

     BoostingRegressionModel → MLWritable