| Class | Description |
|---|---|
| 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.
|