Package

org.apache.spark.mllib

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This package contains the default implementation of the decision tree algorithm, which supports:

Source
package.scala
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  1. class DecisionTree extends Serializable with Logging

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    A class which implements a decision tree learning algorithm for classification and regression.

    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" )
  2. class GradientBoostedTrees extends Serializable with Logging

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    A class that implements Stochastic Gradient Boosting for regression and binary classification.

    A class that implements Stochastic Gradient Boosting for regression and binary classification.

    The implementation is based upon: J.H. Friedman. "Stochastic Gradient Boosting." 1999.

    Notes on Gradient Boosting vs. TreeBoost:

    • This implementation is for Stochastic Gradient Boosting, not for TreeBoost.
    • Both algorithms learn tree ensembles by minimizing loss functions.
    • TreeBoost (Friedman, 1999) additionally modifies the outputs at tree leaf nodes based on the loss function, whereas the original gradient boosting method does not.
      • When the loss is SquaredError, these methods give the same result, but they could differ for other loss functions.
    Annotations
    @Since( "1.2.0" )

Value Members

  1. object DecisionTree extends Serializable with Logging

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    Annotations
    @Since( "1.0.0" )
  2. object GradientBoostedTrees extends Logging with Serializable

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    @Since( "1.2.0" )
  3. object RandomForest extends Serializable with Logging

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    @Since( "1.2.0" )
  4. package configuration

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  5. package impurity

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  6. package loss

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  7. package model

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