org.apache.spark.mllib.tree

DecisionTree

class DecisionTree extends Serializable with Logging

A class which implements a decision tree learning algorithm for classification and regression. It supports both continuous and categorical features.

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@Since( "1.0.0" )
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Instance Constructors

  1. new DecisionTree(strategy: Strategy)

    strategy

    The configuration parameters for the tree algorithm which specify the type of decision tree (classification or regression), feature type (continuous, categorical), depth of the tree, quantile calculation strategy, etc.

    Annotations
    @Since( "1.0.0" )

Value Members

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

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

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  13. def initializeLogIfNecessary(isInterpreter: Boolean): Unit

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  15. def isTraceEnabled(): Boolean

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  16. def log: Logger

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

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  18. def logDebug(msg: ⇒ String): Unit

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

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  20. def logError(msg: ⇒ String): Unit

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

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  22. def logInfo(msg: ⇒ String): Unit

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  23. def logName: String

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

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  25. def logTrace(msg: ⇒ String): Unit

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

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  29. final def notify(): Unit

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  30. final def notifyAll(): Unit

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  31. def run(input: RDD[LabeledPoint]): DecisionTreeModel

    Method to train a decision tree model over an RDD

    Method to train a decision tree model over an RDD

    input

    Training data: RDD of org.apache.spark.mllib.regression.LabeledPoint.

    returns

    DecisionTreeModel that can be used for prediction.

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    @Since( "1.2.0" )

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

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