rules

This category contains 10 nodes.

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.

Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules).

Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes.

Class for generating a PART decision list.

Class for building and using a PRISM rule set for classification.

The implementation of a RIpple-DOwn Rule learner.

Class for building and using a 0-R classifier.