Rule Induction and RIPPER

A decision tree can be read as a set of if-then rules, one rule per path from root to leaf, but those rules are forced to share structure with each other, since they all branch off the same tree. Rule induction algorithms instead build a classifier directly as an ordered list of independent if-then rules, each one covering whatever data it covers, with no shared tree structure required.

How RIPPER works

RIPPER (Repeated Incremental Pruning to Produce Error Reduction) builds rules for the minority class first, using a separate-and-conquer strategy: grow a rule that's as accurate as possible on a held-out growing set, prune it back using a different held-out set to guard against overfitting, then remove the examples the rule now correctly covers and start a new rule on what remains. Once every example is covered, it runs a final optimization pass that tries regrowing each rule in the context of the others, since a rule that looked best in isolation isn't always best once the full rule list is considered together.

Worked example

Classifying loan applications as default/no-default, RIPPER might produce a rule list like: "if credit score < 580, predict default," then on the remaining applicants, "if debt-to-income > 45% and score < 650, predict default," and finally a default prediction of "no default" for anyone the earlier rules didn't catch. Each rule is readable on its own by a loan officer, unlike a coefficient in a logistic regression or a path through a deep tree, which is why rule induction remains popular wherever a decision needs to be explained to a regulator or a customer.