RandomForest

Value Members

1. final def !=(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

2. final def !=(arg0: Any): Boolean

   Definition Classes

   Any

3. final def ##(): Int

   Definition Classes

   AnyRef → Any

4. final def ==(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

5. final def ==(arg0: Any): Boolean

   Definition Classes

   Any

6. final def asInstanceOf[T0]: T0

   Definition Classes

   Any

7. def clone(): AnyRef

   Attributes

   protected[java.lang]

   Definition Classes

   AnyRef

   Annotations

   @throws( ... )

8. final def eq(arg0: AnyRef): Boolean

   Definition Classes

   AnyRef

9. def equals(arg0: Any): Boolean

   Definition Classes

   AnyRef → Any

10. def finalize(): Unit

    Attributes

    protected[java.lang]

    Definition Classes

    AnyRef

    Annotations

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

11. final def getClass(): Class[_]

    Definition Classes

    AnyRef → Any

12. def hashCode(): Int

    Definition Classes

    AnyRef → Any

13. final def isInstanceOf[T0]: Boolean

    Definition Classes

    Any

14. def isTraceEnabled(): Boolean

    Attributes

    protected

    Definition Classes

    Logging

15. def log: Logger

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

17. def logDebug(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

19. def logError(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

21. def logInfo(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

22. def logName: String

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

24. def logTrace(msg: ⇒ String): Unit

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Attributes

    protected

    Definition Classes

    Logging

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

    Definition Classes

    AnyRef

28. final def notify(): Unit

    Definition Classes

    AnyRef

29. final def notifyAll(): Unit

    Definition Classes

    AnyRef

30. val supportedFeatureSubsetStrategies: Array[String]

    List of supported feature subset sampling strategies.

    List of supported feature subset sampling strategies.

    Annotations

    @Since( "1.2.0" )

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

    Definition Classes

    AnyRef

32. def toString(): String

    Definition Classes

    AnyRef → Any

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

    Annotations

    @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. E.g., an entry (n -> 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: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 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

    a random forest model that can be used for prediction

    Annotations

    @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: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "sqrt".

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    a random forest model that can be used for prediction

    Annotations

    @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

    Annotations

    @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. E.g., an entry (n -> 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: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "onethird".

    impurity

    Criterion used for information gain calculation. Supported values: "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

    a random forest model 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: "auto", "all", "sqrt", "log2", "onethird". If "auto" is set, this parameter is set based on numTrees: if numTrees == 1, set to "all"; if numTrees > 1 (forest) set to "onethird".

    seed

    Random seed for bootstrapping and choosing feature subsets.

    returns

    a random forest model that can be used for prediction

    Annotations

    @Since( "1.2.0" )

39. final def wait(): Unit

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )

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

    Definition Classes

    AnyRef

    Annotations

    @throws( ... )