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.
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