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