| Class | Description |
|---|---|
| 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.
|