Advanced Topics in Artificial Intelligence

Model trees—decision trees with linear models at the leaf nodes—have recently emerged as an ax;curate method for numeric prediction that produces understandable models. However, it is known that decision lists—ordered sets of If-Then rules—have the potential to be more compact and therefore more understandable than their tree counterparts. We present an algorithm for inducing simple, accurate decision lists from model trees. Model trees axe built repeatedly and the best rule is selected at e£ich iteration. This method produces rule sets that £ire as accurate but smaller than the model tree constructed from the entire dataset. Experimentai results for various heuristics which attempt to find a compromise between rule accuracy and rule coverage are reported. We show that our method produces comparably accurate and smaller rule sets than the commercial state-of-the-art rule learning system Cubist.