Holmes, GeoffreyHall, Mark A.Frank, EibeFoo, Norman2024-12-122024-12-121999Holmes, G., Hall, M., Frank, E. (1999). Generating Rule Sets from Model Trees. In: Foo, N. (eds) Advanced Topics in Artificial Intelligence. AI 1999. Lecture Notes in Computer Science, vol 1747. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46695-9_13-540-66822-50302-9743https://hdl.handle.net/10289/17089waiting for verificationModel trees—decision trees with linear models at the leaf nodes—have recently emerged as an accurate 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 are built repeatedly and the best rule is selected at each iteration. This method produces rule sets that are as accurate but smaller than the model tree constructed from the entire dataset. Experimental 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.enThis is an author’s accepted version of a conference paper published in Advanced Topics in Artificial Intelligence. AI 1999. Lecture Notes in Computer Science, vol 1747. © 1999 Springer Nature.http://creativecommons.org/licenses/by/4.0/science & technologytechnologyArtificial Intelligencecomputer scienceChapter in Book10.1007/3-540-46695-9_11611-334946 Information and computing sciences