class GBMClassifier extends ProbabilisticClassifier[Vector, GBMClassifier, 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, ProbabilisticClassifier[Vector, GBMClassifier, GBMClassificationModel], ProbabilisticClassifierParams, HasThresholds, HasProbabilityCol, Classifier[Vector, GBMClassifier, GBMClassificationModel], ClassifierParams, HasRawPredictionCol, Predictor[Vector, GBMClassifier, GBMClassificationModel], PredictorParams, HasPredictionCol, HasFeaturesCol, HasLabelCol, Estimator[GBMClassificationModel], PipelineStage, Logging, Params, Serializable, Identifiable, AnyRef, Any
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Inherited
  1. GBMClassifier
  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. ProbabilisticClassifier
  18. ProbabilisticClassifierParams
  19. HasThresholds
  20. HasProbabilityCol
  21. Classifier
  22. ClassifierParams
  23. HasRawPredictionCol
  24. Predictor
  25. PredictorParams
  26. HasPredictionCol
  27. HasFeaturesCol
  28. HasLabelCol
  29. Estimator
  30. PipelineStage
  31. Logging
  32. Params
  33. Serializable
  34. Identifiable
  35. AnyRef
  36. Any
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Visibility
  1. Public
  2. Protected

Instance Constructors

  1. new GBMClassifier()
  2. new GBMClassifier(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. 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[_]): GBMClassifier.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): GBMClassifier
    Definition Classes
    GBMClassifier → Predictor → Estimator → 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[_], numClasses: Int): RDD[Instance]
    Attributes
    protected
    Definition Classes
    ClassifierParams
  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[_], numClasses: Int): RDD[LabeledPoint]
    Attributes
    protected
    Definition Classes
    Classifier
  22. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]
    Attributes
    protected
    Definition Classes
    Predictor
  23. final def extractParamMap(): ParamMap
    Definition Classes
    Params
  24. final def extractParamMap(extra: ParamMap): ParamMap
    Definition Classes
    Params
  25. final val featuresCol: Param[String]
    Definition Classes
    HasFeaturesCol
  26. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.Throwable])
  27. def fit(dataset: Dataset[_]): GBMClassificationModel
    Definition Classes
    Predictor → Estimator
  28. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GBMClassificationModel]
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  29. def fit(dataset: Dataset[_], paramMap: ParamMap): GBMClassificationModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0")
  30. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBMClassificationModel
    Definition Classes
    Estimator
    Annotations
    @Since("2.0.0") @varargs()
  31. def fitBaseLearner(baseLearner: EnsembleRegressorType, labelColName: String, featuresColName: String, predictionColName: String, weightColName: Option[String])(df: DataFrame): EnsemblePredictionModelType
    Attributes
    protected
    Definition Classes
    HasBaseLearner
  32. final def get[T](param: Param[T]): Option[T]
    Definition Classes
    Params
  33. final def getAggregationDepth: Int
    Definition Classes
    HasAggregationDepth
  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
    GBMClassifierParams
  40. final def getLabelCol: String
    Definition Classes
    HasLabelCol
  41. def getLearningRate: Double

    Definition Classes
    GBMParams
  42. def getLoss: String

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

    Definition Classes
    HasNumBaseLearners
  45. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int): Int
    Attributes
    protected
    Definition Classes
    Classifier
  46. def getNumRounds: Int

    Definition Classes
    GBMParams
  47. def getOptimizedWeights: Boolean

    Definition Classes
    GBMParams
  48. final def getOrDefault[T](param: Param[T]): T
    Definition Classes
    Params
  49. def getParallelism: Int
    Definition Classes
    HasParallelism
  50. def getParam(paramName: String): Param[Any]
    Definition Classes
    Params
  51. final def getPredictionCol: String
    Definition Classes
    HasPredictionCol
  52. final def getProbabilityCol: String
    Definition Classes
    HasProbabilityCol
  53. final def getRawPredictionCol: String
    Definition Classes
    HasRawPredictionCol
  54. def getReplacement: Boolean

    Definition Classes
    HasSubBag
  55. final def getSeed: Long
    Definition Classes
    HasSeed
  56. def getSubsampleRatio: Double

    Definition Classes
    HasSubBag
  57. def getSubspaceRatio: Double

    Definition Classes
    HasSubBag
  58. def getThresholds: Array[Double]
    Definition Classes
    HasThresholds
  59. final def getTol: Double
    Definition Classes
    HasTol
  60. def getUpdates: String

    Definition Classes
    GBMParams
  61. final def getValidationIndicatorCol: String
    Definition Classes
    HasValidationIndicatorCol
  62. final def getValidationTol: Double

    Definition Classes
    GBMParams
  63. final def getWeightCol: String
    Definition Classes
    HasWeightCol
  64. final def hasDefault[T](param: Param[T]): Boolean
    Definition Classes
    Params
  65. def hasParam(paramName: String): Boolean
    Definition Classes
    Params
  66. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  67. 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
  68. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  69. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  70. final def isDefined(param: Param[_]): Boolean
    Definition Classes
    Params
  71. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  72. final def isSet(param: Param[_]): Boolean
    Definition Classes
    Params
  73. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  74. final val labelCol: Param[String]
    Definition Classes
    HasLabelCol
  75. val learningRate: Param[Double]

    param for the learning rate of the algorithm

    param for the learning rate of the algorithm

    Definition Classes
    GBMParams
  76. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  77. def logDebug(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logDebug(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logError(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logError(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logInfo(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  82. def logInfo(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  84. def logTrace(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logTrace(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. def logWarning(msg: => String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  87. def logWarning(msg: => String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  88. 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
  89. final val maxIter: IntParam
    Definition Classes
    HasMaxIter
  90. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  91. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  92. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  93. 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
  94. 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
  95. val optimizedWeights: Param[Boolean]

    param for using optimized weights in GBM

    param for using optimized weights in GBM

    Definition Classes
    GBMParams
  96. val parallelism: IntParam
    Definition Classes
    HasParallelism
  97. lazy val params: Array[Param[_]]
    Definition Classes
    Params
  98. final val predictionCol: Param[String]
    Definition Classes
    HasPredictionCol
  99. final val probabilityCol: Param[String]
    Definition Classes
    HasProbabilityCol
  100. final val rawPredictionCol: Param[String]
    Definition Classes
    HasRawPredictionCol
  101. val replacement: Param[Boolean]

    param for whether samples are drawn with replacement

    param for whether samples are drawn with replacement

    Definition Classes
    HasSubBag
  102. 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.")
  103. final val seed: LongParam
    Definition Classes
    HasSeed
  104. final def set(paramPair: ParamPair[_]): GBMClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  105. final def set(param: String, value: Any): GBMClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  106. final def set[T](param: Param[T], value: T): GBMClassifier.this.type
    Definition Classes
    Params
  107. def setAggregationDepth(value: Int): GBMClassifier.this.type

  108. def setBaseLearner(value: EnsembleRegressorType): GBMClassifier.this.type
  109. def setCheckpointInterval(value: Int): GBMClassifier.this.type

  110. final def setDefault(paramPairs: ParamPair[_]*): GBMClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  111. final def setDefault[T](param: Param[T], value: T): GBMClassifier.this.type
    Attributes
    protected
    Definition Classes
    Params
  112. def setFeaturesCol(value: String): GBMClassifier
    Definition Classes
    Predictor
  113. def setInitStrategy(value: String): GBMClassifier.this.type

  114. def setLabelCol(value: String): GBMClassifier
    Definition Classes
    Predictor
  115. def setLearningRate(value: Double): GBMClassifier.this.type

  116. def setLoss(value: String): GBMClassifier.this.type

  117. def setMaxIter(value: Int): GBMClassifier.this.type

  118. def setNumBaseLearners(value: Int): GBMClassifier.this.type

  119. def setNumRounds(value: Int): GBMClassifier.this.type

  120. def setOptimizedWeights(value: Boolean): GBMClassifier.this.type

  121. def setParallelism(value: Int): GBMClassifier.this.type

  122. def setPredictionCol(value: String): GBMClassifier
    Definition Classes
    Predictor
  123. def setProbabilityCol(value: String): GBMClassifier
    Definition Classes
    ProbabilisticClassifier
  124. def setRawPredictionCol(value: String): GBMClassifier
    Definition Classes
    Classifier
  125. def setReplacement(value: Boolean): GBMClassifier.this.type

  126. def setSeed(value: Long): GBMClassifier.this.type

  127. def setSubsampleRatio(value: Double): GBMClassifier.this.type

  128. def setSubspaceRatio(value: Double): GBMClassifier.this.type

  129. def setThresholds(value: Array[Double]): GBMClassifier
    Definition Classes
    ProbabilisticClassifier
  130. def setTol(value: Double): GBMClassifier.this.type

  131. def setUpdates(value: String): GBMClassifier.this.type

  132. def setValidationIndicatorCol(value: String): GBMClassifier.this.type

  133. def setValidationTol(value: Double): GBMClassifier.this.type

  134. def setWeightCol(value: String): GBMClassifier.this.type

  135. def slice(indices: Array[Int]): (Vector) => Vector
    Attributes
    protected
    Definition Classes
    HasSubBag
  136. 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
  137. def subspace(subspaceRatio: Double, numFeatures: Int, seed: Long): Array[Int]
    Attributes
    protected
    Definition Classes
    HasSubBag
  138. 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
  139. final def synchronized[T0](arg0: => T0): T0
    Definition Classes
    AnyRef
  140. val thresholds: DoubleArrayParam
    Definition Classes
    HasThresholds
  141. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  142. final val tol: DoubleParam
    Definition Classes
    HasTol
  143. def train(dataset: Dataset[_]): GBMClassificationModel
    Attributes
    protected
    Definition Classes
    GBMClassifier → Predictor
  144. def transformSchema(schema: StructType): StructType
    Definition Classes
    Predictor → PipelineStage
  145. def transformSchema(schema: StructType, logging: Boolean): StructType
    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  146. val uid: String
    Definition Classes
    GBMClassifier → Identifiable
  147. 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
  148. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType
    Attributes
    protected
    Definition Classes
    ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  149. def validateLabel(label: Double, numClasses: Int): Unit
    Attributes
    protected
    Definition Classes
    Classifier
  150. def validateNumClasses(numClasses: Int): Unit
    Attributes
    protected
    Definition Classes
    Classifier
  151. final val validationIndicatorCol: Param[String]
    Definition Classes
    HasValidationIndicatorCol
  152. 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

  153. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  154. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException])
  155. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws(classOf[java.lang.InterruptedException]) @native()
  156. final val weightCol: Param[String]
    Definition Classes
    HasWeightCol
  157. def write: MLWriter
    Definition Classes
    GBMClassifier → 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 ProbabilisticClassifier[Vector, GBMClassifier, GBMClassificationModel]

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from Classifier[Vector, GBMClassifier, GBMClassificationModel]

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from Predictor[Vector, GBMClassifier, GBMClassificationModel]

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from Estimator[GBMClassificationModel]

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

Ungrouped