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| 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. |
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