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1.  final def !=(arg0: Any): Boolean

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2.  final def ##(): Int

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3.  final def ==(arg0: Any): Boolean

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4.  final def asInstanceOf[T0]: T0

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

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7.  def equals(arg0: Any): Boolean

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8.  def finalize(): Unit

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9.  final def getClass(): Class[_]

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10.  def hashCode(): Int

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

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

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13.  final def isInstanceOf[T0]: Boolean

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14.  def isTraceEnabled(): Boolean

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15.  def log: Logger

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

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

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

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

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

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

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22.  def logName: String

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

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

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

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

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

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28.  final def notify(): Unit

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29.  final def notifyAll(): Unit

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30.  val supportedFeatureSubsetStrategies: Array[String]

    List of supported feature subset sampling strategies.

    List of supported feature subset sampling strategies.

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    @Since( "1.2.0" )

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

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32.  def toString(): String

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33.  def trainClassifier(input: JavaRDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Integer, Integer], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int): RandomForestModel

    Java-friendly API for org.apache.spark.mllib.tree.RandomForest.trainClassifier

    Java-friendly API for org.apache.spark.mllib.tree.RandomForest.trainClassifier

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    @Since( "1.2.0" )

34.  def trainClassifier(input: RDD[LabeledPoint], numClasses: Int, categoricalFeaturesInfo: Map[Int, Int], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int = Utils.random.nextInt()): RandomForestModel

    Method to train a decision tree model for binary or multiclass classification.

    Method to train a decision tree model for binary or multiclass classification.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}.

    numClasses

    Number of classes for classification.

    categoricalFeaturesInfo

    Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "sqrt".

    impurity

    Criterion used for information gain calculation. Supported values: "gini" (recommended) or "entropy".

    maxDepth

    Maximum depth of the tree (e.g. depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 4)

    maxBins

    Maximum number of bins used for splitting features (suggested value: 100)

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

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    @Since( "1.2.0" )

35.  def trainClassifier(input: RDD[LabeledPoint], strategy: Strategy, numTrees: Int, featureSubsetStrategy: String, seed: Int): RandomForestModel

    Method to train a decision tree model for binary or multiclass classification.

    Method to train a decision tree model for binary or multiclass classification.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels should take values {0, 1, ..., numClasses-1}.

    strategy

    Parameters for training each tree in the forest.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "sqrt".

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

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    @Since( "1.2.0" )

36.  def trainRegressor(input: JavaRDD[LabeledPoint], categoricalFeaturesInfo: Map[Integer, Integer], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int): RandomForestModel

    Java-friendly API for org.apache.spark.mllib.tree.RandomForest.trainRegressor

    Java-friendly API for org.apache.spark.mllib.tree.RandomForest.trainRegressor

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    @Since( "1.2.0" )

37.  def trainRegressor(input: RDD[LabeledPoint], categoricalFeaturesInfo: Map[Int, Int], numTrees: Int, featureSubsetStrategy: String, impurity: String, maxDepth: Int, maxBins: Int, seed: Int = Utils.random.nextInt()): RandomForestModel

    Method to train a decision tree model for regression.

    Method to train a decision tree model for regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels are real numbers.

    categoricalFeaturesInfo

    Map storing arity of categorical features. An entry (n to k) indicates that feature n is categorical with k categories indexed from 0: {0, 1, ..., k-1}.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "onethird".

    impurity

    Criterion used for information gain calculation. The only supported value for regression is "variance".

    maxDepth

    Maximum depth of the tree. (e.g., depth 0 means 1 leaf node, depth 1 means 1 internal node + 2 leaf nodes). (suggested value: 4)

    maxBins

    Maximum number of bins used for splitting features. (suggested value: 100)

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

    Annotations

    @Since( "1.2.0" )

38.  def trainRegressor(input: RDD[LabeledPoint], strategy: Strategy, numTrees: Int, featureSubsetStrategy: String, seed: Int): RandomForestModel

    Method to train a decision tree model for regression.

    Method to train a decision tree model for regression.

    input

    Training dataset: RDD of org.apache.spark.mllib.regression.LabeledPoint. Labels are real numbers.

    strategy

    Parameters for training each tree in the forest.

    numTrees

    Number of trees in the random forest.

    featureSubsetStrategy

    Number of features to consider for splits at each node. Supported values: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees is greater than 1 (forest) set to "onethird".

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    RandomForestModel that can be used for prediction.

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    @Since( "1.2.0" )

39.  final def wait(): Unit

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

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

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