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[_]): RandomForestClassifier.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[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @native() @throws( ... )

10.  def copy(extra: ParamMap): RandomForestClassifier

    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

    RandomForestClassifier → 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 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

18.  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

19.  final def extractParamMap(): ParamMap

    extractParamMap with no extra values.

    extractParamMap with no extra values.

    Definition Classes

    Params

20.  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

21.  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 > 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

22.  final val featuresCol: Param[String]

    Param for features column name.

    Param for features column name.

    Definition Classes

    HasFeaturesCol

23.  def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

24.  def fit(dataset: Dataset[_]): RandomForestClassificationModel

    Fits a model to the input data.

    Fits a model to the input data.

    Definition Classes

    Predictor → Estimator

25.  def fit(dataset: Dataset[_], paramMaps: Array[ParamMap]): Seq[RandomForestClassificationModel]

    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" )

26.  def fit(dataset: Dataset[_], paramMap: ParamMap): RandomForestClassificationModel

    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" )

27.  def fit(dataset: Dataset[_], firstParamPair: ParamPair[_], otherParamPairs: ParamPair[_]*): RandomForestClassificationModel

    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()

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

    TreeClassifierParams

36.  final def getLabelCol: String
    Definition Classes

    HasLabelCol

37.  final def getMaxBins: Int
    Definition Classes

    DecisionTreeParams

38.  final def getMaxDepth: Int
    Definition Classes

    DecisionTreeParams

39.  final def getMaxMemoryInMB: Int
    Definition Classes

    DecisionTreeParams

40.  final def getMinInfoGain: Double
    Definition Classes

    DecisionTreeParams

41.  final def getMinInstancesPerNode: Int
    Definition Classes

    DecisionTreeParams

42.  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

43.  final def getNumTrees: Int
    Definition Classes

    RandomForestParams

44.  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

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

    Gets a param by its name.

    Gets a param by its name.

    Definition Classes

    Params

46.  final def getPredictionCol: String
    Definition Classes

    HasPredictionCol

47.  final def getProbabilityCol: String
    Definition Classes

    HasProbabilityCol

48.  final def getRawPredictionCol: String
    Definition Classes

    HasRawPredictionCol

49.  final def getSeed: Long
    Definition Classes

    HasSeed

50.  final def getSubsamplingRate: Double
    Definition Classes

    TreeEnsembleParams

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

    HasThresholds

52.  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

53.  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

54.  def hashCode(): Int

    Definition Classes

    AnyRef → Any

    Annotations

    @native()

55.  final val impurity: Param[String]

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

    Criterion used for information gain calculation (case-insensitive). Supported: "entropy" and "gini". (default = gini)

    Definition Classes

    TreeClassifierParams

56.  def initializeLogIfNecessary(isInterpreter: Boolean, silent: Boolean = false): Boolean

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

58.  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

59.  final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

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

    Checks whether a param is explicitly set.

    Checks whether a param is explicitly set.

    Definition Classes

    Params

61.  def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

62.  final val labelCol: Param[String]

    Param for label column name.

    Param for label column name.

    Definition Classes

    HasLabelCol

63.  def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

70.  def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

75.  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 >= 2 and >= number of categories in any categorical feature. (default = 32)

    Definition Classes

    DecisionTreeParams

76.  final val maxDepth: IntParam

    Maximum depth of the tree (>= 0).

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

    Definition Classes

    DecisionTreeParams

77.  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

78.  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 >= 0.0. (default = 0.0)

    Definition Classes

    DecisionTreeParams

79.  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. Should be >= 1. (default = 1)

    Definition Classes

    DecisionTreeParams

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

    Definition Classes

    AnyRef

81.  final def notify(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

82.  final def notifyAll(): Unit

    Definition Classes

    AnyRef

    Annotations

    @native()

83.  final val numTrees: IntParam

    Number of trees to train (>= 1).

    Number of trees to train (>= 1). If 1, then no bootstrapping is used. If > 1, then bootstrapping is done. TODO: Change to always do bootstrapping (simpler). SPARK-7130 (default = 20)

    Note: The reason that we cannot add this to both GBT and RF (i.e. in TreeEnsembleParams) is the param maxIter controls how many trees a GBT has. The semantics in the algorithms are a bit different.

    Definition Classes

    RandomForestParams

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.  final val predictionCol: Param[String]

    Param for prediction column name.

    Param for prediction column name.

    Definition Classes

    HasPredictionCol

86.  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

87.  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

88.  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( ... )

89.  final val seed: LongParam

    Param for random seed.

    Param for random seed.

    Definition Classes

    HasSeed

90.  final def set(paramPair: ParamPair[_]): RandomForestClassifier.this.type

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Attributes

    protected

    Definition Classes

    Params

91.  final def set(param: String, value: Any): RandomForestClassifier.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

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

    Sets a parameter in the embedded param map.

    Sets a parameter in the embedded param map.

    Definition Classes

    Params

93.  def setCacheNodeIds(value: Boolean): RandomForestClassifier.this.type
    Annotations

    @Since( "1.4.0" )

94.  def setCheckpointInterval(value: Int): RandomForestClassifier.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" )

95.  final def setDefault(paramPairs: ParamPair[_]*): RandomForestClassifier.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

96.  final def setDefault[T](param: Param[T], value: T): RandomForestClassifier.this.type

    Sets a default value for a param.

    Sets a default value for a param.

    param

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

    value

    the default value

    Attributes

    protected

    Definition Classes

    Params

97.  def setFeatureSubsetStrategy(value: String): RandomForestClassifier.this.type
    Annotations

    @Since( "1.4.0" )

98.  def setFeaturesCol(value: String): RandomForestClassifier
    Definition Classes

    Predictor

99.  def setImpurity(value: String): RandomForestClassifier.this.type
    Annotations

    @Since( "1.4.0" )

100.  def setLabelCol(value: String): RandomForestClassifier
     Definition Classes

     Predictor

101.  def setMaxBins(value: Int): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

102.  def setMaxDepth(value: Int): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

103.  def setMaxMemoryInMB(value: Int): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

104.  def setMinInfoGain(value: Double): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

105.  def setMinInstancesPerNode(value: Int): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

106.  def setNumTrees(value: Int): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

107.  def setPredictionCol(value: String): RandomForestClassifier
     Definition Classes

     Predictor

108.  def setProbabilityCol(value: String): RandomForestClassifier
     Definition Classes

     ProbabilisticClassifier

109.  def setRawPredictionCol(value: String): RandomForestClassifier
     Definition Classes

     Classifier

110.  def setSeed(value: Long): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

111.  def setSubsamplingRate(value: Double): RandomForestClassifier.this.type
     Annotations

     @Since( "1.4.0" )

112.  def setThresholds(value: Array[Double]): RandomForestClassifier
     Definition Classes

     ProbabilisticClassifier

113.  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

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

     Definition Classes

     AnyRef

115.  final 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

116.  def toString(): String

     Definition Classes

     Identifiable → AnyRef → Any

117.  def train(dataset: Dataset[_]): RandomForestClassificationModel

     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

     RandomForestClassifier → Predictor

118.  def transformSchema(schema: StructType): StructType

     :: DeveloperApi ::

     :: DeveloperApi ::

     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

119.  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()

120.  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

     RandomForestClassifier → Identifiable

     Annotations

     @Since( "1.4.0" )

121.  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

     ProbabilisticClassifierParams → ClassifierParams → PredictorParams

122.  final def wait(): Unit

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @throws( ... )

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

     Definition Classes

     AnyRef

     Annotations

     @native() @throws( ... )

125.  def write: MLWriter

     Returns an MLWriter instance for this ML instance.

     Returns an MLWriter instance for this ML instance.

     Definition Classes

     DefaultParamsWritable → MLWritable