package tree
This package contains the default implementation of the decision tree algorithm, which supports:
 binary classification,
 regression,
 information loss calculation with entropy and Gini for classification and variance for regression,
 both continuous and categorical features.
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class
DecisionTree extends Serializable with Logging
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.
 Annotations
 @Since( "1.0.0" )

class
GradientBoostedTrees extends Serializable with Logging
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

object
DecisionTree extends Serializable with Logging
 Annotations
 @Since( "1.0.0" )

object
GradientBoostedTrees extends Logging with Serializable
 Annotations
 @Since( "1.2.0" )

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