org.apache.spark.mllib.tree

RandomForest

object RandomForest extends Serializable with Logging

Linear Supertypes
Logging, Serializable, Serializable, AnyRef, Any
Ordering
  1. Alphabetic
  2. By inheritance
Inherited
  1. RandomForest
  2. Logging
  3. Serializable
  4. Serializable
  5. AnyRef
  6. Any
  1. Hide All
  2. Show all
Learn more about member selection
Visibility
  1. Public
  2. All

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.

  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

  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

  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

  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

  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

  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

  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( ... )

Inherited from Logging

Inherited from Serializable

Inherited from Serializable

Inherited from AnyRef

Inherited from Any

Ungrouped