Packages

class GBTClassifier extends ProbabilisticClassifier[Vector, GBTClassifier, GBTClassificationModel] with GBTClassifierParams with DefaultParamsWritable with Logging

Gradient-Boosted Trees (GBTs) (http://en.wikipedia.org/wiki/Gradient_boosting) learning algorithm for classification. It supports binary labels, as well as both continuous and categorical features.

The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

Notes on Gradient Boosting vs. TreeBoost:

  • This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
  • Both algorithms learn tree ensembles by minimizing loss functions.
  • TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
  • We expect to implement TreeBoost in the future: [https://issues.apache.org/jira/browse/SPARK-4240]
Annotations
@Since( "1.4.0" )
Source
GBTClassifier.scala
Note

Multiclass labels are not currently supported.

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Inherited
  1. GBTClassifier
  2. DefaultParamsWritable
  3. MLWritable
  4. GBTClassifierParams
  5. HasVarianceImpurity
  6. TreeEnsembleClassifierParams
  7. GBTParams
  8. HasValidationIndicatorCol
  9. HasStepSize
  10. HasMaxIter
  11. TreeEnsembleParams
  12. DecisionTreeParams
  13. HasWeightCol
  14. HasSeed
  15. HasCheckpointInterval
  16. ProbabilisticClassifier
  17. ProbabilisticClassifierParams
  18. HasThresholds
  19. HasProbabilityCol
  20. Classifier
  21. ClassifierParams
  22. HasRawPredictionCol
  23. Predictor
  24. PredictorParams
  25. HasPredictionCol
  26. HasFeaturesCol
  27. HasLabelCol
  28. Estimator
  29. PipelineStage
  30. Logging
  31. Params
  32. Serializable
  33. Serializable
  34. Identifiable
  35. AnyRef
  36. Any
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Instance Constructors

  1. new GBTClassifier()
    Annotations
    @Since( "1.4.0" )
  2. new GBTClassifier(uid: String)
    Annotations
    @Since( "1.4.0" )

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

    An alias for getOrDefault().

    An alias for getOrDefault().

    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. final val cacheNodeIds: BooleanParam

    If false, the algorithm will pass trees to executors to match instances with nodes.

    If false, the algorithm will pass trees to executors to match instances with nodes. If true, the algorithm will cache node IDs for each instance. Caching can speed up training of deeper trees. Users can set how often should the cache be checkpointed or disable it by setting checkpointInterval. (default = false)

    Definition Classes
    DecisionTreeParams
  7. final val checkpointInterval: IntParam

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1).

    Param for set checkpoint interval (>= 1) or disable checkpoint (-1). E.g. 10 means that the cache will get checkpointed every 10 iterations. Note: this setting will be ignored if the checkpoint directory is not set in the SparkContext.

    Definition Classes
    HasCheckpointInterval
  8. final def clear(param: Param[_]): GBTClassifier.this.type

    Clears the user-supplied value for the input param.

    Clears the user-supplied value for the input param.

    Definition Classes
    Params
  9. def clone(): AnyRef
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  10. def copy(extra: ParamMap): GBTClassifier

    Creates a copy of this instance with the same UID and some extra params.

    Creates a copy of this instance with the same UID and some extra params. Subclasses should implement this method and set the return type properly. See defaultCopy().

    Definition Classes
    GBTClassifierPredictorEstimatorPipelineStageParams
    Annotations
    @Since( "1.4.1" )
  11. def copyValues[T <: Params](to: T, extra: ParamMap = ParamMap.empty): T

    Copies param values from this instance to another instance for params shared by them.

    Copies param values from this instance to another instance for params shared by them.

    This handles default Params and explicitly set Params separately. Default Params are copied from and to defaultParamMap, and explicitly set Params are copied from and to paramMap. Warning: This implicitly assumes that this Params instance and the target instance share the same set of default Params.

    to

    the target instance, which should work with the same set of default Params as this source instance

    extra

    extra params to be copied to the target's paramMap

    returns

    the target instance with param values copied

    Attributes
    protected
    Definition Classes
    Params
  12. final def defaultCopy[T <: Params](extra: ParamMap): T

    Default implementation of copy with extra params.

    Default implementation of copy with extra params. It tries to create a new instance with the same UID. Then it copies the embedded and extra parameters over and returns the new instance.

    Attributes
    protected
    Definition Classes
    Params
  13. final def eq(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  14. def equals(arg0: Any): Boolean
    Definition Classes
    AnyRef → Any
  15. def explainParam(param: Param[_]): String

    Explains a param.

    Explains a param.

    param

    input param, must belong to this instance.

    returns

    a string that contains the input param name, doc, and optionally its default value and the user-supplied value

    Definition Classes
    Params
  16. def explainParams(): String

    Explains all params of this instance.

    Explains all params of this instance. See explainParam().

    Definition Classes
    Params
  17. def extractInstances(dataset: Dataset[_], numClasses: Int): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    ClassifierParams
  18. def extractInstances(dataset: Dataset[_], validateInstance: (Instance) ⇒ Unit): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types. Validate the output instances with the given function.

    Attributes
    protected
    Definition Classes
    PredictorParams
  19. def extractInstances(dataset: Dataset[_]): RDD[Instance]

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol, weightCol(if any) and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    PredictorParams
  20. def extractLabeledPoints(dataset: Dataset[_], numClasses: Int): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    dataset

    DataFrame with columns for labels (org.apache.spark.sql.types.NumericType) and features (Vector).

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
    Note

    Throws SparkException if any label is a non-integer or is negative

  21. def extractLabeledPoints(dataset: Dataset[_]): RDD[LabeledPoint]

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Extract labelCol and featuresCol from the given dataset, and put it in an RDD with strong types.

    Attributes
    protected
    Definition Classes
    Predictor
  22. final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes
    Params
  23. final def extractParamMap(extra: ParamMap): ParamMap

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Extracts the embedded default param values and user-supplied values, and then merges them with extra values from input into a flat param map, where the latter value is used if there exist conflicts, i.e., with ordering: default param values less than user-supplied values less than extra.

    Definition Classes
    Params
  24. final val featureSubsetStrategy: Param[String]

    The number of features to consider for splits at each tree node.

    The number of features to consider for splits at each tree node. Supported options:

    • "auto": Choose automatically for task: If numTrees == 1, set to "all." If numTrees greater than 1 (forest), set to "sqrt" for classification and to "onethird" for regression.
    • "all": use all features
    • "onethird": use 1/3 of the features
    • "sqrt": use sqrt(number of features)
    • "log2": use log2(number of features)
    • "n": when n is in the range (0, 1.0], use n * number of features. When n is in the range (1, number of features), use n features. (default = "auto")

    These various settings are based on the following references:

    • log2: tested in Breiman (2001)
    • sqrt: recommended by Breiman manual for random forests
    • The defaults of sqrt (classification) and onethird (regression) match the R randomForest package.
    Definition Classes
    TreeEnsembleParams
    See also

    Breiman (2001)

    Breiman manual for random forests

  25. final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes
    HasFeaturesCol
  26. def finalize(): Unit
    Attributes
    protected[lang]
    Definition Classes
    AnyRef
    Annotations
    @throws( classOf[java.lang.Throwable] )
  27. def fit(dataset: Dataset[_]): GBTClassificationModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes
    PredictorEstimator
  28. def fit(dataset: Dataset[_], paramMaps: Seq[ParamMap]): Seq[GBTClassificationModel]

    Fits multiple models to the input data with multiple sets of parameters.

    Fits multiple models to the input data with multiple sets of parameters. The default implementation uses a for loop on each parameter map. Subclasses could override this to optimize multi-model training.

    dataset

    input dataset

    paramMaps

    An array of parameter maps. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted models, matching the input parameter maps

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  29. def fit(dataset: Dataset[_], paramMap: ParamMap): GBTClassificationModel

    Fits a single model to the input data with provided parameter map.

    Fits a single model to the input data with provided parameter map.

    dataset

    input dataset

    paramMap

    Parameter map. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" )
  30. def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): GBTClassificationModel

    Fits a single model to the input data with optional parameters.

    Fits a single model to the input data with optional parameters.

    dataset

    input dataset

    firstParamPair

    the first param pair, overrides embedded params

    otherParamPairs

    other param pairs. These values override any specified in this Estimator's embedded ParamMap.

    returns

    fitted model

    Definition Classes
    Estimator
    Annotations
    @Since( "2.0.0" ) @varargs()
  31. final def get[T](param: Param[T]): Option[T]

    Optionally returns the user-supplied value of a param.

    Optionally returns the user-supplied value of a param.

    Definition Classes
    Params
  32. final def getCacheNodeIds: Boolean

    Definition Classes
    DecisionTreeParams
  33. final def getCheckpointInterval: Int

    Definition Classes
    HasCheckpointInterval
  34. final def getClass(): Class[_]
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  35. final def getDefault[T](param: Param[T]): Option[T]

    Gets the default value of a parameter.

    Gets the default value of a parameter.

    Definition Classes
    Params
  36. final def getFeatureSubsetStrategy: String

    Definition Classes
    TreeEnsembleParams
  37. final def getFeaturesCol: String

    Definition Classes
    HasFeaturesCol
  38. final def getImpurity: String

    Definition Classes
    HasVarianceImpurity
  39. final def getLabelCol: String

    Definition Classes
    HasLabelCol
  40. final def getLeafCol: String

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  41. def getLossType: String

    Definition Classes
    GBTClassifierParams
  42. final def getMaxBins: Int

    Definition Classes
    DecisionTreeParams
  43. final def getMaxDepth: Int

    Definition Classes
    DecisionTreeParams
  44. final def getMaxIter: Int

    Definition Classes
    HasMaxIter
  45. final def getMaxMemoryInMB: Int

    Definition Classes
    DecisionTreeParams
  46. final def getMinInfoGain: Double

    Definition Classes
    DecisionTreeParams
  47. final def getMinInstancesPerNode: Int

    Definition Classes
    DecisionTreeParams
  48. final def getMinWeightFractionPerNode: Double

    Definition Classes
    DecisionTreeParams
  49. def getNumClasses(dataset: Dataset[_], maxNumClasses: Int = 100): Int

    Get the number of classes.

    Get the number of classes. This looks in column metadata first, and if that is missing, then this assumes classes are indexed 0,1,...,numClasses-1 and computes numClasses by finding the maximum label value.

    Label validation (ensuring all labels are integers >= 0) needs to be handled elsewhere, such as in extractLabeledPoints().

    dataset

    Dataset which contains a column labelCol

    maxNumClasses

    Maximum number of classes allowed when inferred from data. If numClasses is specified in the metadata, then maxNumClasses is ignored.

    returns

    number of classes

    Attributes
    protected
    Definition Classes
    Classifier
    Exceptions thrown

    IllegalArgumentException if metadata does not specify numClasses, and the actual numClasses exceeds maxNumClasses

  50. final def getOrDefault[T](param: Param[T]): T

    Gets the value of a param in the embedded param map or its default value.

    Gets the value of a param in the embedded param map or its default value. Throws an exception if neither is set.

    Definition Classes
    Params
  51. def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes
    Params
  52. final def getPredictionCol: String

    Definition Classes
    HasPredictionCol
  53. final def getProbabilityCol: String

    Definition Classes
    HasProbabilityCol
  54. final def getRawPredictionCol: String

    Definition Classes
    HasRawPredictionCol
  55. final def getSeed: Long

    Definition Classes
    HasSeed
  56. final def getStepSize: Double

    Definition Classes
    HasStepSize
  57. final def getSubsamplingRate: Double

    Definition Classes
    TreeEnsembleParams
  58. def getThresholds: Array[Double]

    Definition Classes
    HasThresholds
  59. final def getValidationIndicatorCol: String

    Definition Classes
    HasValidationIndicatorCol
  60. final def getValidationTol: Double

    Definition Classes
    GBTParams
    Annotations
    @Since( "2.4.0" )
  61. final def getWeightCol: String

    Definition Classes
    HasWeightCol
  62. final def hasDefault[T](param: Param[T]): Boolean

    Tests whether the input param has a default value set.

    Tests whether the input param has a default value set.

    Definition Classes
    Params
  63. def hasParam(paramName: String): Boolean

    Tests whether this instance contains a param with a given name.

    Tests whether this instance contains a param with a given name.

    Definition Classes
    Params
  64. def hashCode(): Int
    Definition Classes
    AnyRef → Any
    Annotations
    @native()
  65. final val impurity: Param[String]

    Criterion used for information gain calculation (case-insensitive).

    Criterion used for information gain calculation (case-insensitive). This impurity type is used in DecisionTreeRegressor, RandomForestRegressor, GBTRegressor and GBTClassifier (since GBTClassificationModel is internally composed of DecisionTreeRegressionModels). Supported: "variance". (default = variance)

    Definition Classes
    HasVarianceImpurity
  66. def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  67. def initializeLogIfNecessary(isInterpreter: Boolean): Unit
    Attributes
    protected
    Definition Classes
    Logging
  68. final def isDefined(param: Param[_]): Boolean

    Checks whether a param is explicitly set or has a default value.

    Checks whether a param is explicitly set or has a default value.

    Definition Classes
    Params
  69. final def isInstanceOf[T0]: Boolean
    Definition Classes
    Any
  70. final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes
    Params
  71. def isTraceEnabled(): Boolean
    Attributes
    protected
    Definition Classes
    Logging
  72. final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes
    HasLabelCol
  73. final val leafCol: Param[String]

    Leaf indices column name.

    Leaf indices column name. Predicted leaf index of each instance in each tree by preorder. (default = "")

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  74. def log: Logger
    Attributes
    protected
    Definition Classes
    Logging
  75. def logDebug(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  76. def logDebug(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  77. def logError(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  78. def logError(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  79. def logInfo(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  80. def logInfo(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  81. def logName: String
    Attributes
    protected
    Definition Classes
    Logging
  82. def logTrace(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  83. def logTrace(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  84. def logWarning(msg: ⇒ String, throwable: Throwable): Unit
    Attributes
    protected
    Definition Classes
    Logging
  85. def logWarning(msg: ⇒ String): Unit
    Attributes
    protected
    Definition Classes
    Logging
  86. val lossType: Param[String]

    Loss function which GBT tries to minimize.

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

    Definition Classes
    GBTClassifierParams
  87. final val maxBins: IntParam

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node.

    Maximum number of bins used for discretizing continuous features and for choosing how to split on features at each node. More bins give higher granularity. Must be at least 2 and at least number of categories in any categorical feature. (default = 32)

    Definition Classes
    DecisionTreeParams
  88. final val maxDepth: IntParam

    Maximum depth of the tree (nonnegative).

    Maximum depth of the tree (nonnegative). E.g., depth 0 means 1 leaf node; depth 1 means 1 internal node + 2 leaf nodes. (default = 5)

    Definition Classes
    DecisionTreeParams
  89. final val maxIter: IntParam

    Param for maximum number of iterations (>= 0).

    Param for maximum number of iterations (>= 0).

    Definition Classes
    HasMaxIter
  90. final val maxMemoryInMB: IntParam

    Maximum memory in MB allocated to histogram aggregation.

    Maximum memory in MB allocated to histogram aggregation. If too small, then 1 node will be split per iteration, and its aggregates may exceed this size. (default = 256 MB)

    Definition Classes
    DecisionTreeParams
  91. final val minInfoGain: DoubleParam

    Minimum information gain for a split to be considered at a tree node.

    Minimum information gain for a split to be considered at a tree node. Should be at least 0.0. (default = 0.0)

    Definition Classes
    DecisionTreeParams
  92. final val minInstancesPerNode: IntParam

    Minimum number of instances each child must have after split.

    Minimum number of instances each child must have after split. If a split causes the left or right child to have fewer than minInstancesPerNode, the split will be discarded as invalid. Must be at least 1. (default = 1)

    Definition Classes
    DecisionTreeParams
  93. final val minWeightFractionPerNode: DoubleParam

    Minimum fraction of the weighted sample count that each child must have after split.

    Minimum fraction of the weighted sample count that each child must have after split. If a split causes the fraction of the total weight in the left or right child to be less than minWeightFractionPerNode, the split will be discarded as invalid. Should be in the interval [0.0, 0.5). (default = 0.0)

    Definition Classes
    DecisionTreeParams
  94. final def ne(arg0: AnyRef): Boolean
    Definition Classes
    AnyRef
  95. final def notify(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  96. final def notifyAll(): Unit
    Definition Classes
    AnyRef
    Annotations
    @native()
  97. lazy val params: Array[Param[_]]

    Returns all params sorted by their names.

    Returns all params sorted by their names. The default implementation uses Java reflection to list all public methods that have no arguments and return Param.

    Definition Classes
    Params
    Note

    Developer should not use this method in constructor because we cannot guarantee that this variable gets initialized before other params.

  98. final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes
    HasPredictionCol
  99. final val probabilityCol: Param[String]

    Param for Column name for predicted class conditional probabilities.

    Param for Column name for predicted class conditional probabilities. Note: Not all models output well-calibrated probability estimates! These probabilities should be treated as confidences, not precise probabilities.

    Definition Classes
    HasProbabilityCol
  100. final val rawPredictionCol: Param[String]

    Param for raw prediction (a.k.a.

    Param for raw prediction (a.k.a. confidence) column name.

    Definition Classes
    HasRawPredictionCol
  101. def save(path: String): Unit

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Saves this ML instance to the input path, a shortcut of write.save(path).

    Definition Classes
    MLWritable
    Annotations
    @Since( "1.6.0" ) @throws( ... )
  102. final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes
    HasSeed
  103. final def set(paramPair: ParamPair[_]): GBTClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  104. final def set(param: String, value: Any): GBTClassifier.this.type

    Sets a parameter (by name) in the embedded param map.

    Sets a parameter (by name) in the embedded param map.

    Attributes
    protected
    Definition Classes
    Params
  105. final def set[T](param: Param[T], value: T): GBTClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes
    Params
  106. def setCacheNodeIds(value: Boolean): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  107. def setCheckpointInterval(value: Int): GBTClassifier.this.type

    Specifies how often to checkpoint the cached node IDs.

    Specifies how often to checkpoint the cached node IDs. E.g. 10 means that the cache will get checkpointed every 10 iterations. This is only used if cacheNodeIds is true and if the checkpoint directory is set in org.apache.spark.SparkContext. Must be at least 1. (default = 10)

    Annotations
    @Since( "1.4.0" )
  108. final def setDefault(paramPairs: ParamPair[_]*): GBTClassifier.this.type

    Sets default values for a list of params.

    Sets default values for a list of params.

    Note: Java developers should use the single-parameter setDefault. Annotating this with varargs can cause compilation failures due to a Scala compiler bug. See SPARK-9268.

    paramPairs

    a list of param pairs that specify params and their default values to set respectively. Make sure that the params are initialized before this method gets called.

    Attributes
    protected
    Definition Classes
    Params
  109. final def setDefault[T](param: Param[T], value: T): GBTClassifier.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

    param to set the default value. Make sure that this param is initialized before this method gets called.

    value

    the default value

    Attributes
    protected
    Definition Classes
    Params
  110. def setFeatureSubsetStrategy(value: String): GBTClassifier.this.type

    Annotations
    @Since( "2.3.0" )
  111. def setFeaturesCol(value: String): GBTClassifier

    Definition Classes
    Predictor
  112. def setImpurity(value: String): GBTClassifier.this.type

    The impurity setting is ignored for GBT models.

    The impurity setting is ignored for GBT models. Individual trees are built using impurity "Variance."

    Annotations
    @Since( "1.4.0" )
  113. def setLabelCol(value: String): GBTClassifier

    Definition Classes
    Predictor
  114. final def setLeafCol(value: String): GBTClassifier.this.type

    Definition Classes
    DecisionTreeParams
    Annotations
    @Since( "3.0.0" )
  115. def setLossType(value: String): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  116. def setMaxBins(value: Int): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  117. def setMaxDepth(value: Int): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  118. def setMaxIter(value: Int): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  119. def setMaxMemoryInMB(value: Int): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  120. def setMinInfoGain(value: Double): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  121. def setMinInstancesPerNode(value: Int): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  122. def setMinWeightFractionPerNode(value: Double): GBTClassifier.this.type

    Annotations
    @Since( "3.0.0" )
  123. def setPredictionCol(value: String): GBTClassifier

    Definition Classes
    Predictor
  124. def setProbabilityCol(value: String): GBTClassifier

    Definition Classes
    ProbabilisticClassifier
  125. def setRawPredictionCol(value: String): GBTClassifier

    Definition Classes
    Classifier
  126. def setSeed(value: Long): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  127. def setStepSize(value: Double): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  128. def setSubsamplingRate(value: Double): GBTClassifier.this.type

    Annotations
    @Since( "1.4.0" )
  129. def setThresholds(value: Array[Double]): GBTClassifier

    Definition Classes
    ProbabilisticClassifier
  130. def setValidationIndicatorCol(value: String): GBTClassifier.this.type

    Annotations
    @Since( "2.4.0" )
  131. def setWeightCol(value: String): GBTClassifier.this.type

    Sets the value of param weightCol.

    Sets the value of param weightCol. If this is not set or empty, we treat all instance weights as 1.0. By default the weightCol is not set, so all instances have weight 1.0.

    Annotations
    @Since( "3.0.0" )
  132. final val stepSize: DoubleParam

    Param for Step size (a.k.a.

    Param for Step size (a.k.a. learning rate) in interval (0, 1] for shrinking the contribution of each estimator. (default = 0.1)

    Definition Classes
    GBTParams → HasStepSize
  133. final val subsamplingRate: DoubleParam

    Fraction of the training data used for learning each decision tree, in range (0, 1].

    Fraction of the training data used for learning each decision tree, in range (0, 1]. (default = 1.0)

    Definition Classes
    TreeEnsembleParams
  134. final def synchronized[T0](arg0: ⇒ T0): T0
    Definition Classes
    AnyRef
  135. val thresholds: DoubleArrayParam

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class.

    Param for Thresholds in multi-class classification to adjust the probability of predicting each class. Array must have length equal to the number of classes, with values > 0 excepting that at most one value may be 0. The class with largest value p/t is predicted, where p is the original probability of that class and t is the class's threshold.

    Definition Classes
    HasThresholds
  136. def toString(): String
    Definition Classes
    Identifiable → AnyRef → Any
  137. def train(dataset: Dataset[_]): GBTClassificationModel

    Train a model using the given dataset and parameters.

    Train a model using the given dataset and parameters. Developers can implement this instead of fit() to avoid dealing with schema validation and copying parameters into the model.

    dataset

    Training dataset

    returns

    Fitted model

    Attributes
    protected
    Definition Classes
    GBTClassifierPredictor
  138. def transformSchema(schema: StructType): StructType

    Check transform validity and derive the output schema from the input schema.

    Check transform validity and derive the output schema from the input schema.

    We check validity for interactions between parameters during transformSchema and raise an exception if any parameter value is invalid. Parameter value checks which do not depend on other parameters are handled by Param.validate().

    Typical implementation should first conduct verification on schema change and parameter validity, including complex parameter interaction checks.

    Definition Classes
    PredictorPipelineStage
  139. def transformSchema(schema: StructType, logging: Boolean): StructType

    :: DeveloperApi ::

    :: DeveloperApi ::

    Derives the output schema from the input schema and parameters, optionally with logging.

    This should be optimistic. If it is unclear whether the schema will be valid, then it should be assumed valid until proven otherwise.

    Attributes
    protected
    Definition Classes
    PipelineStage
    Annotations
    @DeveloperApi()
  140. val uid: String

    An immutable unique ID for the object and its derivatives.

    An immutable unique ID for the object and its derivatives.

    Definition Classes
    GBTClassifierIdentifiable
    Annotations
    @Since( "1.4.0" )
  141. def validateAndTransformSchema(schema: StructType, fitting: Boolean, featuresDataType: DataType): StructType

    Validates and transforms the input schema with the provided param map.

    Validates and transforms the input schema with the provided param map.

    schema

    input schema

    fitting

    whether this is in fitting

    featuresDataType

    SQL DataType for FeaturesType. E.g., VectorUDT for vector features.

    returns

    output schema

    Attributes
    protected
    Definition Classes
    TreeEnsembleClassifierParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams
  142. def validateLabel(label: Double, numClasses: Int): Unit

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    Validates the label on the classifier is a valid integer in the range [0, numClasses).

    label

    The label to validate.

    numClasses

    Number of classes label can take. Labels must be integers in the range [0, numClasses).

    Attributes
    protected
    Definition Classes
    Classifier
  143. def validateNumClasses(numClasses: Int): Unit

    Validates that number of classes is greater than zero.

    Validates that number of classes is greater than zero.

    numClasses

    Number of classes label can take.

    Attributes
    protected
    Definition Classes
    Classifier
  144. final val validationIndicatorCol: Param[String]

    Param for name of the column that indicates whether each row is for training or for validation.

    Param for name of the column that indicates whether each row is for training or for validation. False indicates training; true indicates validation..

    Definition Classes
    HasValidationIndicatorCol
  145. 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
    GBTParams
    Annotations
    @Since( "2.4.0" )
    See also

    validationIndicatorCol

  146. final def wait(): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  147. final def wait(arg0: Long, arg1: Int): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... )
  148. final def wait(arg0: Long): Unit
    Definition Classes
    AnyRef
    Annotations
    @throws( ... ) @native()
  149. final val weightCol: Param[String]

    Param for weight column name.

    Param for weight column name. If this is not set or empty, we treat all instance weights as 1.0.

    Definition Classes
    HasWeightCol
  150. def write: MLWriter

    Returns an MLWriter instance for this ML instance.

    Returns an MLWriter instance for this ML instance.

    Definition Classes
    DefaultParamsWritableMLWritable

Inherited from DefaultParamsWritable

Inherited from MLWritable

Inherited from GBTClassifierParams

Inherited from HasVarianceImpurity

Inherited from TreeEnsembleClassifierParams

Inherited from GBTParams

Inherited from HasValidationIndicatorCol

Inherited from HasStepSize

Inherited from HasMaxIter

Inherited from TreeEnsembleParams

Inherited from DecisionTreeParams

Inherited from HasWeightCol

Inherited from HasSeed

Inherited from HasCheckpointInterval

Inherited from ProbabilisticClassifierParams

Inherited from HasThresholds

Inherited from HasProbabilityCol

Inherited from ClassifierParams

Inherited from HasRawPredictionCol

Inherited from PredictorParams

Inherited from HasPredictionCol

Inherited from HasFeaturesCol

Inherited from HasLabelCol

Inherited from PipelineStage

Inherited from Logging

Inherited from Params

Inherited from Serializable

Inherited from Serializable

Inherited from Identifiable

Inherited from AnyRef

Inherited from Any

Parameters

A list of (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

Members

Parameter setters

Parameter getters

(expert-only) Parameters

A list of advanced, expert-only (hyper-)parameter keys this algorithm can take. Users can set and get the parameter values through setters and getters, respectively.

(expert-only) Parameter setters

(expert-only) Parameter getters