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

    GBTClassifier → Predictor → Estimator → PipelineStage → Params

    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

    Predictor → Estimator

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[ml]

     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

     GBTClassifier → Predictor

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

     Predictor → PipelineStage

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

     GBTClassifier → Identifiable

     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

     DefaultParamsWritable → MLWritable