Frank, EibeWang, YongInglis, Stuart J.Holmes, GeoffreyWitten, Ian H.2025-01-282025-01-281998Frank, E., Wang, Y., Inglis, S., Holmes, G., & Witten, I. H. (1998). Using model trees for classification. Machine Learning, 32(1), 63-76.0885-6125https://hdl.handle.net/10289/17137Waiting for verification - 19 Jn 2024Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful technique for predicting continuous numeric values. They can be applied to classification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5′, based on Quinlan’s M5, generates more accurate classifiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.EnglishThis is an author’s accepted version of an article published in Machine Learning. © 1998. Kluwer Academic Publishers.Science & TechnologyTechnologyComputer Science, Artificial IntelligenceComputer Sciencemodel treesclassification algorithmsM5C5.0decision treesJournal Article10.1023/A:10074213021494611 Machine learning