There are 5701 nodes that can be used as predessesor
for a node with an input port of type Generic Port.
Class for generating a decision tree with naive Bayes classifiers at the leaves.
Fast decision tree learner.
Class for constructing a forest of random trees.
Class for constructing a tree that considers K randomly chosen attributes at each node.
Class implementing minimal cost-complexity pruning.
Interactively classify through visual means.
This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels.
Class for building and using a simple decision table majority classifier.
This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER).
Generates a decision list for regression problems using separate-and-conquer.