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[_]): GBTClassificationModel.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): GBTClassificationModel

    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

    GBTClassificationModel → Model → Transformer → PipelineStage → Params

    Annotations

    @Since( "1.4.0" )

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 evaluateEachIteration(dataset: Dataset[_]): Array[Double]

    Method to compute error or loss for every iteration of gradient boosting.

    Method to compute error or loss for every iteration of gradient boosting.

    dataset

    Dataset for validation.

    Annotations

    @Since( "2.4.0" )

16.  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

17.  def explainParams(): String

    Explains all params of this instance.

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

    Definition Classes

    Params

18.  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

19.  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

20.  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

21.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

22.  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

23.  lazy val featureImportances: Vector

    Estimate of the importance of each feature.

    Estimate of the importance of each feature.

    Each feature's importance is the average of its importance across all trees in the ensemble The importance vector is normalized to sum to 1. This method is suggested by Hastie et al. (Hastie, Tibshirani, Friedman. "The Elements of Statistical Learning, 2nd Edition." 2001.) and follows the implementation from scikit-learn.

    See DecisionTreeClassificationModel.featureImportances

    Annotations

    @Since( "2.0.0" )

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 featuresDataType: DataType

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    Returns the SQL DataType corresponding to the FeaturesType type parameter.

    This is used by validateAndTransformSchema(). This workaround is needed since SQL has different APIs for Scala and Java.

    The default value is VectorUDT, but it may be overridden if FeaturesType is not Vector.

    Attributes

    protected

    Definition Classes

    PredictionModel

27.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

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

28.  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

29.  final def getCacheNodeIds: Boolean
    Definition Classes

    DecisionTreeParams

30.  final def getCheckpointInterval: Int
    Definition Classes

    HasCheckpointInterval

31.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

32.  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

33.  final def getFeatureSubsetStrategy: String
    Definition Classes

    TreeEnsembleParams

34.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

35.  final def getImpurity: String
    Definition Classes

    HasVarianceImpurity

36.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

37.  final def getLeafCol: String
    Definition Classes

    DecisionTreeParams

    Annotations

    @Since( "3.0.0" )

38.  def getLossType: String
    Definition Classes

    GBTClassifierParams

39.  final def getMaxBins: Int
    Definition Classes

    DecisionTreeParams

40.  final def getMaxDepth: Int
    Definition Classes

    DecisionTreeParams

41.  final def getMaxIter: Int
    Definition Classes

    HasMaxIter

42.  final def getMaxMemoryInMB: Int
    Definition Classes

    DecisionTreeParams

43.  final def getMinInfoGain: Double
    Definition Classes

    DecisionTreeParams

44.  final def getMinInstancesPerNode: Int
    Definition Classes

    DecisionTreeParams

45.  final def getMinWeightFractionPerNode: Double
    Definition Classes

    DecisionTreeParams

46.  val getNumTrees: Int

    Number of trees in ensemble

    Number of trees in ensemble

    Annotations

    @Since( "2.0.0" )

47.  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

48.  def getParam(paramName: String): Param[Any]

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

49.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

50.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

51.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

52.  final def getSeed: Long
    Definition Classes

    HasSeed

53.  final def getStepSize: Double
    Definition Classes

    HasStepSize

54.  final def getSubsamplingRate: Double
    Definition Classes

    TreeEnsembleParams

55.  def getThresholds: Array[Double]
    Definition Classes

    HasThresholds

56.  final def getValidationIndicatorCol: String
    Definition Classes

    HasValidationIndicatorCol

57.  final def getValidationTol: Double
    Definition Classes

    GBTParams

    Annotations

    @Since( "2.4.0" )

58.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

59.  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

60.  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

61.  def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes

    Model

62.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

63.  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

64.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean): Boolean

    Attributes

    protected

    Definition Classes

    Logging

65.  def initializeLogIfNecessary(isInterpreter: Boolean): Unit

    Attributes

    protected

    Definition Classes

    Logging

66.  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

67.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

68.  final def isSet(param: Param[_]): Boolean

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

69.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

70.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

71.  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" )

72.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

73.  def logDebug(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

74.  def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

76.  def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

78.  def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

79.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

80.  def logTrace(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

81.  def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

82.  def logWarning(msg: ⇒ String, throwable: Throwable): Unit

    Attributes

    protected

    Definition Classes

    Logging

83.  def logWarning(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

84.  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

85.  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

86.  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

87.  final val maxIter: IntParam

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

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

    Definition Classes

    HasMaxIter

88.  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

89.  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

90.  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

91.  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

92.  final def ne(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

93.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

94.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

95.  val numClasses: Int

    Number of classes (values which the label can take).

    Number of classes (values which the label can take).

    Definition Classes

    GBTClassificationModel → ClassificationModel

    Annotations

    @Since( "2.2.0" )

96.  val numFeatures: Int

    Returns the number of features the model was trained on.

    Returns the number of features the model was trained on. If unknown, returns -1

    Definition Classes

    GBTClassificationModel → PredictionModel

    Annotations

    @Since( "1.6.0" )

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.  var parent: Estimator[GBTClassificationModel]

    The parent estimator that produced this model.

    The parent estimator that produced this model.

    Definition Classes

    Model

    Note

    For ensembles' component Models, this value can be null.

99.  def predict(features: Vector): Double

    Predict label for the given features.

    Predict label for the given features. This method is used to implement transform() and output predictionCol.

    This default implementation for classification predicts the index of the maximum value from predictRaw().

    Definition Classes

    GBTClassificationModel → ClassificationModel → PredictionModel

100.  def predictLeaf(features: Vector): Vector
     returns

     The indices of the leaves corresponding to the feature vector. Leaves are indexed in pre-order from 0.

     Definition Classes

     TreeEnsembleModel

101.  def predictProbability(features: Vector): Vector

     Predict the probability of each class given the features.

     Predict the probability of each class given the features. These predictions are also called class conditional probabilities.

     This internal method is used to implement transform() and output probabilityCol.

     returns

     Estimated class conditional probabilities

     Definition Classes

     ProbabilisticClassificationModel

     Annotations

     @Since( "3.0.0" )

102.  def predictRaw(features: Vector): Vector

     Raw prediction for each possible label.

     Raw prediction for each possible label. The meaning of a "raw" prediction may vary between algorithms, but it intuitively gives a measure of confidence in each possible label (where larger = more confident). This internal method is used to implement transform() and output rawPredictionCol.

     returns

     vector where element i is the raw prediction for label i. This raw prediction may be any real number, where a larger value indicates greater confidence for that label.

     Definition Classes

     GBTClassificationModel → ClassificationModel

     Annotations

     @Since( "3.0.0" )

103.  final val predictionCol: Param[String]

     Param for prediction column name.

     Param for prediction column name.

     Definition Classes

     HasPredictionCol

104.  def probability2prediction(probability: Vector): Double

     Given a vector of class conditional probabilities, select the predicted label.

     Given a vector of class conditional probabilities, select the predicted label. This supports thresholds which favor particular labels.

     returns

     predicted label

     Attributes

     protected

     Definition Classes

     ProbabilisticClassificationModel

105.  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

106.  def raw2prediction(rawPrediction: Vector): Double

     Given a vector of raw predictions, select the predicted label.

     Given a vector of raw predictions, select the predicted label. This may be overridden to support thresholds which favor particular labels.

     returns

     predicted label

     Attributes

     protected

     Definition Classes

     ProbabilisticClassificationModel → ClassificationModel

107.  def raw2probability(rawPrediction: Vector): Vector

     Non-in-place version of raw2probabilityInPlace()

     Non-in-place version of raw2probabilityInPlace()

     Attributes

     protected

     Definition Classes

     ProbabilisticClassificationModel

108.  def raw2probabilityInPlace(rawPrediction: Vector): Vector

     Estimate the probability of each class given the raw prediction, doing the computation in-place.

     Estimate the probability of each class given the raw prediction, doing the computation in-place. These predictions are also called class conditional probabilities.

     This internal method is used to implement transform() and output probabilityCol.

     returns

     Estimated class conditional probabilities (modified input vector)

     Attributes

     protected

     Definition Classes

     GBTClassificationModel → ProbabilisticClassificationModel

109.  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

110.  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( ... )

111.  final val seed: LongParam

     Param for random seed.

     Param for random seed.

     Definition Classes

     HasSeed

112.  final def set(paramPair: ParamPair[_]): GBTClassificationModel.this.type

     Sets a parameter in the embedded param map.

     Sets a parameter in the embedded param map.

     Attributes

     protected

     Definition Classes

     Params

113.  final def set(param: String, value: Any): GBTClassificationModel.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

114.  final def set[T](param: Param[T], value: T): GBTClassificationModel.this.type

     Sets a parameter in the embedded param map.

     Sets a parameter in the embedded param map.

     Definition Classes

     Params

115.  final def setDefault(paramPairs: ParamPair[_]*): GBTClassificationModel.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

116.  final def setDefault[T](param: Param[T], value: T): GBTClassificationModel.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[ml]

     Definition Classes

     Params

117.  def setFeaturesCol(value: String): GBTClassificationModel
     Definition Classes

     PredictionModel

118.  final def setLeafCol(value: String): GBTClassificationModel.this.type
     Definition Classes

     DecisionTreeParams

     Annotations

     @Since( "3.0.0" )

119.  def setParent(parent: Estimator[GBTClassificationModel]): GBTClassificationModel

     Sets the parent of this model (Java API).

     Sets the parent of this model (Java API).

     Definition Classes

     Model

120.  def setPredictionCol(value: String): GBTClassificationModel
     Definition Classes

     PredictionModel

121.  def setProbabilityCol(value: String): GBTClassificationModel
     Definition Classes

     ProbabilisticClassificationModel

122.  def setRawPredictionCol(value: String): GBTClassificationModel
     Definition Classes

     ClassificationModel

123.  def setThresholds(value: Array[Double]): GBTClassificationModel
     Definition Classes

     ProbabilisticClassificationModel

124.  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

125.  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

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

     Definition Classes

     AnyRef

127.  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

128.  def toDebugString: String

     Full description of model

     Full description of model

     Definition Classes

     TreeEnsembleModel

129.  def toString(): String

     Summary of the model

     Summary of the model

     Definition Classes

     GBTClassificationModel → TreeEnsembleModel → Identifiable → AnyRef → Any

     Annotations

     @Since( "1.4.0" )

130.  lazy val totalNumNodes: Int

     Total number of nodes, summed over all trees in the ensemble.

     Total number of nodes, summed over all trees in the ensemble.

     Definition Classes

     TreeEnsembleModel

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

     Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

     Transforms dataset by reading from featuresCol, and appending new columns as specified by parameters:

     • predicted labels as predictionCol of type Double
     • raw predictions (confidences) as rawPredictionCol of type Vector
     • probability of each class as probabilityCol of type Vector.

     dataset

     input dataset

     returns

     transformed dataset

     Definition Classes

     GBTClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer

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

     Transforms the dataset with provided parameter map as additional parameters.

     Transforms the dataset with provided parameter map as additional parameters.

     dataset

     input dataset

     paramMap

     additional parameters, overwrite embedded params

     returns

     transformed dataset

     Definition Classes

     Transformer

     Annotations

     @Since( "2.0.0" )

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

     Transforms the dataset with optional parameters

     Transforms the dataset with optional parameters

     dataset

     input dataset

     firstParamPair

     the first param pair, overwrite embedded params

     otherParamPairs

     other param pairs, overwrite embedded params

     returns

     transformed dataset

     Definition Classes

     Transformer

     Annotations

     @Since( "2.0.0" ) @varargs()

134.  final def transformImpl(dataset: Dataset[_]): DataFrame

     Definition Classes

     ClassificationModel → PredictionModel

135.  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

     GBTClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage

     Annotations

     @Since( "1.6.0" )

136.  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()

137.  def treeWeights: Array[Double]

     Weights for each tree, zippable with trees

     Weights for each tree, zippable with trees

     Definition Classes

     GBTClassificationModel → TreeEnsembleModel

     Annotations

     @Since( "1.4.0" )

138.  def trees: Array[DecisionTreeRegressionModel]

     Trees in this ensemble.

     Trees in this ensemble. Warning: These have null parent Estimators.

     Definition Classes

     GBTClassificationModel → TreeEnsembleModel

     Annotations

     @Since( "1.4.0" )

139.  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

     GBTClassificationModel → Identifiable

     Annotations

     @Since( "1.6.0" )

140.  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

141.  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

142.  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

143.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

145.  final def wait(arg0: Long): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

146.  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

147.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     GBTClassificationModel → MLWritable

     Annotations

     @Since( "2.0.0" )