|  | ## Rule Induction

#### Synopsis

Learns a pruned set of rules with respect to the information gain.

#### Description

This operator works similar to the propositional rule learner named Repeated Incremental Pruning to Produce Error Reduction (RIPPER, Cohen 1995). Starting with the less prevalent classes, the algorithm iteratively grows and prunes rules until there are no positive examples left or the error rate is greater than 50%.

In the growing phase, for each rule greedily conditions are added to the rule until the rule is perfect (i.e. 100% accurate). The procedure tries every possible value of each attribute and selects the condition with highest information gain.

In the prune phase, for each rule any final sequences of the antecedents is pruned with the pruning metric p/(p+n).

#### Input

- training set: expects: ExampleSet

#### Output

- model:
- exampleSet:

#### Parameters

- criterion: Specifies the used criterion for selecting attributes and numerical splits.
- sample ratio: The sample ratio of training data used for growing and pruning.
- pureness: The desired pureness, i.e. the necessary amount of the major class in a covered subset in order become pure.
- minimal prune benefit: The minimum amount of benefit which must be exceeded over unpruned benefit in order to be pruned.
- use local random seed: Indicates if a local random seed should be used.
- local random seed: Specifies the local random seed

#### ExampleProcess