class GBMClassificationModel extends ProbabilisticClassificationModel[Vector, GBMClassificationModel] with GBMClassifierParams with MLWritable

Source
GBMClassifier.scala
Linear Supertypes
MLWritable, GBMClassifierParams, HasParallelism, GBMParams, HasSubBag, HasSeed, BoostingParams[EnsembleRegressorType], HasAggregationDepth, HasCheckpointInterval, HasBaseLearner[EnsembleRegressorType], HasWeightCol, HasNumBaseLearners, HasValidationIndicatorCol, HasTol, HasMaxIter, ProbabilisticClassificationModel[Vector, GBMClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, ClassificationModel[Vector, GBMClassificationModel], ClassifierParams, HasRawPredictionCol, PredictionModel[Vector, GBMClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Model[GBMClassificationModel], Transformer, PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GBMClassificationModel
  2. MLWritable
  3. GBMClassifierParams
  4. HasParallelism
  5. GBMParams
  6. HasSubBag
  7. HasSeed
  8. BoostingParams
  9. HasAggregationDepth
  10. HasCheckpointInterval
  11. HasBaseLearner
  12. HasWeightCol
  13. HasNumBaseLearners
  14. HasValidationIndicatorCol
  15. HasTol
  16. HasMaxIter
  17. ProbabilisticClassificationModel
  18. ProbabilisticClassifierParams
  19. HasThresholds
  20. HasProbabilityCol
  21. ClassificationModel
  22. ClassifierParams
  23. HasRawPredictionCol
  24. PredictionModel
  25. PredictorParams
  26. HasPredictionCol
  27. HasFeaturesCol
  28. HasLabelCol
  29. Model
  30. Transformer
  31. PipelineStage
  32. Logging
  33. Params
  34. Serializable
  35. Identifiable
  36. AnyRef
  37. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GBMClassificationModel(numClasses: Int, weights: Array[Array[Double]], subspaces: Array[Array[Int]], models: Array[Array[EnsemblePredictionModelType]], init: EnsembleClassificationModelType, dim: Int)
  2. new GBMClassificationModel(uid: String, numClasses: Int, weights: Array[Array[Double]], subspaces: Array[Array[Int]], models: Array[Array[EnsemblePredictionModelType]], init: EnsembleClassificationModelType, dim: Int)

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[_]): GBMClassificationModel.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): GBMClassificationModel
    Definition Classes
    GBMClassificationModel → 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. val dim: Int
  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[_], numClasses: Int): RDD[Instance]
    Attributes
    protected
    Definition Classes
    ClassifierParams
  20. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) => Unit): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  21. def extractInstances(dataset: Dataset[_]): RDD[Instance]
    Attributes
    protected
    Definition Classes
    PredictorParams
  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 featuresDataType: DataType
    Attributes
    protected
    Definition Classes
    PredictionModel
  26. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  27. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  28. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  29. final def getAggregationDepth: Int
    Definition Classes
    HasAggregationDepth
  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
    GBMClassifierParams
  36. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  37. def getLearningRate: Double

    Definition Classes
    GBMParams
  38. def getLoss: String

    Definition Classes
    GBMClassifierParams
  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 getParallelism: Int
    Definition Classes
    HasParallelism
  45. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  46. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  47. final def getProbabilityCol: String
    Definition Classes
    HasProbabilityCol
  48. final def getRawPredictionCol: String
    Definition Classes
    HasRawPredictionCol
  49. def getReplacement: Boolean

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

    Definition Classes
    HasSubBag
  52. def getSubspaceRatio: Double

    Definition Classes
    HasSubBag
  53. def getThresholds: Array[Double]
    Definition Classes
    HasThresholds
  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 hasParent: Boolean
    Definition Classes
    Model
  62. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  63. val init: EnsembleClassificationModelType
  64. val initStrategy: Param[String]

    strategy for the init predictions, can be the class-prior or the uniform distribution.

    strategy for the init predictions, can be the class-prior or the uniform distribution. (case-insensitive) Supported: "uniform", "prior". (default = prior)

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

    param for the learning rate of the algorithm

    param for the learning rate of the algorithm

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

    Loss function which GBM tries to minimize.

    Loss function which GBM tries to minimize. (case-insensitive) Supported: "logloss", "exponential", "bernoulli". (default = logloss)

    Definition Classes
    GBMClassifierParams
  86. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  87. val models: Array[Array[EnsemblePredictionModelType]]
  88. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  89. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  90. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  91. 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
  92. val numClasses: Int
    Definition Classes
    GBMClassificationModel → ClassificationModel
  93. def numFeatures: Int
    Definition Classes
    PredictionModel
    Annotations
    @Since("1.6.0")
  94. val numModels: Int
  95. 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
  96. val optimizedWeights: Param[Boolean]

    param for using optimized weights in GBM

    param for using optimized weights in GBM

    Definition Classes
    GBMParams
  97. val parallelism: IntParam
    Definition Classes
    HasParallelism
  98. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  99. var parent: Estimator[GBMClassificationModel]
    Definition Classes
    Model
  100. def predict(features: Vector): Double
    Definition Classes
    ClassificationModel → PredictionModel
  101. def predictProbability(features: Vector): Vector
    Definition Classes
    ProbabilisticClassificationModel
    Annotations
    @Since("3.0.0")
  102. def predictRaw(features: Vector): Vector
    Definition Classes
    GBMClassificationModel → ClassificationModel
  103. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  104. def probability2prediction(probability: Vector): Double
    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  105. final val probabilityCol: Param[String]
    Definition Classes
    HasProbabilityCol
  106. def raw2prediction(rawPrediction: Vector): Double
    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel
  107. def raw2probability(rawPrediction: Vector): Vector
    Attributes
    protected
    Definition Classes
    ProbabilisticClassificationModel
  108. def raw2probabilityInPlace(rawPrediction: Vector): Vector
    Attributes
    protected
    Definition Classes
    GBMClassificationModel → ProbabilisticClassificationModel
  109. final val rawPredictionCol: Param[String]
    Definition Classes
    HasRawPredictionCol
  110. val replacement: Param[Boolean]

    param for whether samples are drawn with replacement

    param for whether samples are drawn with replacement

    Definition Classes
    HasSubBag
  111. 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.")
  112. final val seed: LongParam
    Definition Classes
    HasSeed
  113. final def set(paramPair: ParamPair[_]): GBMClassificationModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  114. final def set(param: String, value: Any): GBMClassificationModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  115. final def set[T](param: Param[T], value: T): GBMClassificationModel.this.type
    Definition Classes
    Params
  116. final def setDefault(paramPairs: ParamPair[_]*): GBMClassificationModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  117. final def setDefault[T](param: Param[T], value: T): GBMClassificationModel.this.type
    Attributes
    protected
    Definition Classes
    Params
  118. def setFeaturesCol(value: String): GBMClassificationModel
    Definition Classes
    PredictionModel
  119. def setParent(parent: Estimator[GBMClassificationModel]): GBMClassificationModel
    Definition Classes
    Model
  120. def setPredictionCol(value: String): GBMClassificationModel
    Definition Classes
    PredictionModel
  121. def setProbabilityCol(value: String): GBMClassificationModel
    Definition Classes
    ProbabilisticClassificationModel
  122. def setRawPredictionCol(value: String): GBMClassificationModel
    Definition Classes
    ClassificationModel
  123. def setThresholds(value: Array[Double]): GBMClassificationModel
    Definition Classes
    ProbabilisticClassificationModel
  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. val subspaces: Array[Array[Int]]
  129. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  130. val thresholds: DoubleArrayParam
    Definition Classes
    HasThresholds
  131. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  132. final val tol: DoubleParam
    Definition Classes
    HasTol
  133. def transform(dataset: Dataset[_]): DataFrame
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer
  134. def transform(dataset: Dataset[_], paramMap: ParamMap): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0")
  135. def transform(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): DataFrame
    Definition Classes
    Transformer
    Annotations
    @Since("2.0.0") @varargs()
  136. final def transformImpl(dataset: Dataset[_]): DataFrame
    Definition Classes
    ClassificationModel → PredictionModel
  137. def transformSchema(schema: StructType): StructType
    Definition Classes
    ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage
  138. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  139. val uid: String
    Definition Classes
    GBMClassificationModel → Identifiable
  140. 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
  141. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  142. final val validationIndicatorCol: Param[String]
    Definition Classes
    HasValidationIndicatorCol
  143. 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

  144. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  145. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  146. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  147. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  148. val weights: Array[Array[Double]]
  149. def write: MLWriter
    Definition Classes
    GBMClassificationModel → MLWritable

Inherited from MLWritable

Inherited from GBMClassifierParams

Inherited from HasParallelism

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 ProbabilisticClassificationModel[Vector, GBMClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassificationModel[Vector, GBMClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictionModel[Vector, GBMClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Model[GBMClassificationModel]

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