This category contains 16 nodes.

NBTree (3.6) 

Class for generating a decision tree with naive Bayes classifiers at the leaves. For more information, see Ron Kohavi: Scaling Up the Accuracy of […]

RandomForest (3.6) 

Class for constructing a forest of random trees. For more information see: Leo Breiman (2001). Random Forests. Machine Learning. 45(1):5-32.

RandomTree (3.6) 

Class for constructing a tree that considers K randomly chosen attributes at each node. Performs no pruning. Also has an option to allow estimation of […]

REPTree (3.6) 

Fast decision tree learner. Builds a decision/regression tree using information gain/variance and prunes it using reduced-error pruning (with backfitting). […]

SimpleCart (3.6) 

Class implementing minimal cost-complexity pruning. Note when dealing with missing values, use "fractional instances" method instead of surrogate split […]

UserClassifier (3.6) 

Interactively classify through visual means. You are Presented with a scatter graph of the data against two user selectable attributes, as well as a view of […]