tree

Type Members

1. 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" )

2. 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" )