Ignore them (as if they didn’t appear in any condition)
Algorithms that learn decision lists have an advantage
Examples that appear to be difficult at the beginning are left to the end and can be solved
In general, in an easier way
Numerical attributes can be treated in the same way than with decision trees
Pruning
Pruning in the way to evaluate a rule
Consider the probability that a random rule gives equal or worse results than the rule being evaluated
Based in the improvement of the pos covered
Idea
Generate rules that cover only pos examples
Probably these rules are over-especialized
Eliminate the last term added and verify if the rule is better than the previous one
We repeat the process until there are no improvements
Rule Pruning Algorithm
Rule Pruning Algorithm
This algorithm doesn’t guarantee finding the best rules because of 3 main reasons
The algorithm to construct the rules, doesn’t necessarily produces the best rules to be reduced
The reduction of rules starts with the last condition, and that is not necessarily the best order to follow
The reduction of rules finishes when the estimated value changes, which doesn’t guarantee that keeping prunning might improve the estimated value again