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[_]): DecisionTreeClassificationModel.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): DecisionTreeClassificationModel

    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

    DecisionTreeClassificationModel → 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.  lazy val depth: Int

    Depth of the tree.

    Depth of the tree. E.g.: Depth 0 means 1 leaf node. Depth 1 means 1 internal node and 2 leaf nodes.

    Definition Classes

    DecisionTreeModel

14.  final def eq(arg0: AnyRef): Boolean

    Definition Classes

    AnyRef

15.  def equals(arg0: Any): Boolean

    Definition Classes

    AnyRef → Any

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.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

19.  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

20.  lazy val featureImportances: Vector

    Estimate of the importance of each feature.

    Estimate of the importance of each feature.

    This generalizes the idea of "Gini" importance to other losses, following the explanation of Gini importance from "Random Forests" documentation by Leo Breiman and Adele Cutler, and following the implementation from scikit-learn.

    This feature importance is calculated as follows:

    • importance(feature j) = sum (over nodes which split on feature j) of the gain, where gain is scaled by the number of instances passing through node
    • Normalize importances for tree to sum to 1.

    Annotations

    @Since( "2.0.0" )

    Note

    Feature importance for single decision trees can have high variance due to correlated predictor variables. Consider using a RandomForestClassifier to determine feature importance instead.

21.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

22.  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

23.  def finalize(): Unit

    Attributes

    protected[lang]

    Definition Classes

    AnyRef

    Annotations

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

24.  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

25.  final def getCacheNodeIds: Boolean
    Definition Classes

    DecisionTreeParams

26.  final def getCheckpointInterval: Int
    Definition Classes

    HasCheckpointInterval

27.  final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

28.  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

29.  final def getFeaturesCol: String
    Definition Classes

    HasFeaturesCol

30.  final def getImpurity: String
    Definition Classes

    TreeClassifierParams

31.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

32.  final def getLeafCol: String
    Definition Classes

    DecisionTreeParams

    Annotations

    @Since( "3.0.0" )

33.  final def getMaxBins: Int
    Definition Classes

    DecisionTreeParams

34.  final def getMaxDepth: Int
    Definition Classes

    DecisionTreeParams

35.  final def getMaxMemoryInMB: Int
    Definition Classes

    DecisionTreeParams

36.  final def getMinInfoGain: Double
    Definition Classes

    DecisionTreeParams

37.  final def getMinInstancesPerNode: Int
    Definition Classes

    DecisionTreeParams

38.  final def getMinWeightFractionPerNode: Double
    Definition Classes

    DecisionTreeParams

39.  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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

41.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

42.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

43.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

44.  final def getSeed: Long
    Definition Classes

    HasSeed

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

    HasThresholds

46.  final def getWeightCol: String
    Definition Classes

    HasWeightCol

47.  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

48.  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

49.  def hasParent: Boolean

    Indicates whether this Model has a corresponding parent.

    Indicates whether this Model has a corresponding parent.

    Definition Classes

    Model

50.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

51.  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 DecisionTreeClassifier and RandomForestClassifier, Supported: "entropy" and "gini". (default = gini)

    Definition Classes

    TreeClassifierParams

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

54.  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

55.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

57.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

58.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

59.  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" )

60.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

67.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

72.  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

73.  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

74.  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

75.  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

76.  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

77.  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

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

    Definition Classes

    AnyRef

79.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

80.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

81.  val numClasses: Int

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

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

    Definition Classes

    DecisionTreeClassificationModel → ClassificationModel

    Annotations

    @Since( "1.5.0" )

82.  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

    DecisionTreeClassificationModel → PredictionModel

    Annotations

    @Since( "1.6.0" )

83.  def numNodes: Int

    Number of nodes in tree, including leaf nodes.

    Number of nodes in tree, including leaf nodes.

    Definition Classes

    DecisionTreeModel

84.  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.

85.  var parent: Estimator[DecisionTreeClassificationModel]

    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.

86.  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

    DecisionTreeClassificationModel → ClassificationModel → PredictionModel

87.  def predictLeaf(features: Vector): Double
    returns

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

    Definition Classes

    DecisionTreeModel

88.  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" )

89.  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

    DecisionTreeClassificationModel → ClassificationModel

    Annotations

    @Since( "3.0.0" )

90.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

91.  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

92.  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

93.  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

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

    Non-in-place version of raw2probabilityInPlace()

    Non-in-place version of raw2probabilityInPlace()

    Attributes

    protected

    Definition Classes

    ProbabilisticClassificationModel

95.  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

    DecisionTreeClassificationModel → ProbabilisticClassificationModel

96.  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

97.  val rootNode: Node

    Root of the decision tree

    Root of the decision tree

    Definition Classes

    DecisionTreeClassificationModel → DecisionTreeModel

    Annotations

    @Since( "1.4.0" )

98.  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( ... )

99.  final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes

    HasSeed

100.  final def set(paramPair: ParamPair[_]): DecisionTreeClassificationModel.this.type

     Sets a parameter in the embedded param map.

     Sets a parameter in the embedded param map.

     Attributes

     protected

     Definition Classes

     Params

101.  final def set(param: String, value: Any): DecisionTreeClassificationModel.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

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

     Sets a parameter in the embedded param map.

     Sets a parameter in the embedded param map.

     Definition Classes

     Params

103.  final def setDefault(paramPairs: ParamPair[_]*): DecisionTreeClassificationModel.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

104.  final def setDefault[T](param: Param[T], value: T): DecisionTreeClassificationModel.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

105.  def setFeaturesCol(value: String): DecisionTreeClassificationModel
     Definition Classes

     PredictionModel

106.  final def setLeafCol(value: String): DecisionTreeClassificationModel.this.type
     Definition Classes

     DecisionTreeParams

     Annotations

     @Since( "3.0.0" )

107.  def setParent(parent: Estimator[DecisionTreeClassificationModel]): DecisionTreeClassificationModel

     Sets the parent of this model (Java API).

     Sets the parent of this model (Java API).

     Definition Classes

     Model

108.  def setPredictionCol(value: String): DecisionTreeClassificationModel
     Definition Classes

     PredictionModel

109.  def setProbabilityCol(value: String): DecisionTreeClassificationModel
     Definition Classes

     ProbabilisticClassificationModel

110.  def setRawPredictionCol(value: String): DecisionTreeClassificationModel
     Definition Classes

     ClassificationModel

111.  def setThresholds(value: Array[Double]): DecisionTreeClassificationModel
     Definition Classes

     ProbabilisticClassificationModel

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

     Definition Classes

     AnyRef

113.  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

114.  def toDebugString: String

     Full description of model

     Full description of model

     Definition Classes

     DecisionTreeModel

115.  def toString(): String

     Summary of the model

     Summary of the model

     Definition Classes

     DecisionTreeClassificationModel → DecisionTreeModel → Identifiable → AnyRef → Any

     Annotations

     @Since( "1.4.0" )

116.  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

     DecisionTreeClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → Transformer

117.  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" )

118.  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()

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

     Definition Classes

     ClassificationModel → PredictionModel

120.  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

     DecisionTreeClassificationModel → ProbabilisticClassificationModel → ClassificationModel → PredictionModel → PipelineStage

     Annotations

     @Since( "3.0.0" )

121.  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()

122.  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

     DecisionTreeClassificationModel → Identifiable

     Annotations

     @Since( "1.4.0" )

123.  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

     DecisionTreeClassifierParams → ProbabilisticClassifierParams → ClassifierParams → PredictorParams

124.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... ) @native()

127.  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

128.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     DecisionTreeClassificationModel → MLWritable

     Annotations

     @Since( "2.0.0" )