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Class Summary |
| 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 |
| 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. |
| 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. |
| 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. |