Package jsat.classifiers.trees

Interface Summary

Interface	Description
TreeFeatureImportanceInference	This interface exists for implementing the importance of features from tree based models.
TreeLearner	This interface provides a contract that allows for the mutation and pruning of a tree using the TreeNodeVisitor and related classes.

Class Summary

Class	Description
DecisionStump	This class is a 1-rule.
DecisionTree	Creates a decision tree from DecisionStumps.
DecisionTree.Node
ERTrees	Extra Randomized Trees (ERTrees) is an ensemble method built on top of ExtraTree.
ExtraTree	The ExtraTree is an Extremely Randomized Tree.
ID3
ImportanceByUses
ImpurityScore	ImpurityScore provides a measure of the impurity of a set of data points respective to their class labels.
MDA	Mean Decrease in Accuracy (MDA) measures feature importance by applying the classifier for each feature, and corruption one feature at a time as each dataum its pushed through the tree.
MDI	Determines the importance of features by measuring the decrease in impurity caused by each feature used, weighted by the amount of data seen by the node using the feature.
RandomDecisionTree	An extension of Decision Trees, it ignores the given set of features to use- and selects a new random subset of features at each node for use.
RandomForest	Random Forest is an extension of Bagging that is applied only to DecisionTrees.
TreeNodeVisitor	Provides an abstracted mechanism for traversing and predicting from nodes in a tree meant for a supervised learning problem.
TreePruner	Provides post-pruning algorithms for any decision tree that can be altered using the TreeNodeVisitor.

Enum Summary

Enum	Description
ImpurityScore.ImpurityMeasure	Different methods of measuring the impurity in a set of data points based on nominal class labels
TreePruner.PruningMethod	The method of pruning to use