Package weka.classifiers.trees

Class Summary
ADTree Class for generating an alternating decision tree.
BFTree Class for building a best-first decision tree classifier.
DecisionStump Class for building and using a decision stump.
FT Classifier for building 'Functional trees', which are classification trees that could have logistic regression functions at the inner nodes and/or leaves.
Id3 Class for constructing an unpruned decision tree based on the ID3 algorithm.
J48 Class for generating a pruned or unpruned C4.5 decision tree.
J48graft Class for generating a grafted (pruned or unpruned) C4.5 decision tree.
LADTree Class for generating a multi-class alternating decision tree using the LogitBoost strategy.
LMT Classifier for building 'logistic model trees', which are classification trees with logistic regression functions at the leaves.
M5P M5Base.
NBTree Class for generating a decision tree with naive Bayes classifiers at the leaves.

For more information, see

Ron Kohavi: Scaling Up the Accuracy of Naive-Bayes Classifiers: A Decision-Tree Hybrid.
RandomForest Class for constructing a forest of random trees.

For more information see:

Leo Breiman (2001).
RandomTree Class for constructing a tree that considers K randomly chosen attributes at each node.
REPTree Fast decision tree learner.
SimpleCart Class implementing minimal cost-complexity pruning.
Note when dealing with missing values, use "fractional instances" method instead of surrogate split method.

For more information, see:

Leo Breiman, Jerome H.
UserClassifier Interactively classify through visual means.