Frank, E., Wang, Y., Inglis, S., Holmes, G. & Witten, I.H. (1997). Using model trees for classification. (Working paper 97/12). Hamilton, New Zealand: University of Waikato, Department of Computer Science.
Abstract
Model 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.
