| Class | Description |
|---|---|
| DecisionTable |
Class for building and using a simple decision table majority classifier.
|
| 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. to the more sophisticated ones such as MDL
calculation and rule variants generation for each rule in the ruleset.
|
| ZeroR |
Class for building and using a 0-R classifier.
|