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.

Linear Supertypes

AnyRef, Any

class DecisionTree extends Serializable with Logging

:: Experimental :: A class which implements a decision tree learning algorithm for classification and regression.
class GradientBoostedTrees extends Serializable with Logging

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