org.apache.spark.mllib.tree

DecisionTree

class DecisionTree extends Serializable with Logging

:: Experimental :: 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" ) @Experimental()
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Instance Constructors

  1. new DecisionTree(strategy: Strategy)

    strategy

    The configuration parameters for the tree algorithm which specify the type of algorithm (classification, regression, etc.), 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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  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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  28. final def notify(): Unit

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  29. final def notifyAll(): Unit

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  30. 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

    Annotations
    @Since( "1.2.0" )

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