org.apache.spark.mllib.tree

DecisionTree

class DecisionTree extends Serializable with Logging

:: Experimental :: A class that implements a decision tree algorithm for classification and regression. It supports both continuous and categorical features.

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@Experimental()
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Logging, Serializable, Serializable, AnyRef, Any
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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.

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  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 logTrace(msg: ⇒ String, throwable: Throwable): Unit

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

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

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

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  30. def toString(): String

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  31. def train(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

    RDD of org.apache.spark.mllib.regression.LabeledPoint used as training data

    returns

    a DecisionTreeModel that can be used for prediction

  32. final def wait(): Unit

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

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