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| Class Summary | |
|---|---|
| ConjunctiveRule | This class implements a single conjunctive rule learner that can predict for numeric and nominal class labels. A rule consists of antecedents "AND"ed together and the consequent (class value) for the classification/regression. |
| DecisionTable | Class for building and using a simple decision table majority classifier. For more information see: Ron Kohavi: The Power of Decision Tables. |
| DecisionTableHashKey | Class providing hash table keys for DecisionTable |
| DTNB | Class for building and using a decision table/naive bayes hybrid classifier. |
| JRip | This class implements a propositional rule learner, Repeated Incremental Pruning to Produce Error Reduction (RIPPER), which was proposed by William W. |
| M5Rules | Generates a decision list for regression problems using separate-and-conquer. |
| NNge | Nearest-neighbor-like algorithm using non-nested generalized exemplars (which are hyperrectangles that can be viewed as if-then rules). |
| OneR | Class for building and using a 1R classifier; in other words, uses the minimum-error attribute for prediction, discretizing numeric attributes. |
| PART | Class for generating a PART decision list. |
| Prism | Class for building and using a PRISM rule set for classification. |
| Ridor | An implementation of a RIpple-DOwn Rule learner. It generates a default rule first and then the exceptions for the default rule with the least (weighted) error rate. |
| Rule | Abstract class of generic rule |
| RuleStats | This class implements the statistics functions used in the propositional rule learner, from the simpler ones like count of true/false positive/negatives, filter data based on the ruleset, etc. |
| ZeroR | Class for building and using a 0-R classifier. |
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