TECHNICAL NOTE - USING MODEL TREES FOR CLASSIFICATION

Model trees, which are a type of decision tree with linear regression functions at the leaves, form the basis of a recent successful techniq ue for predicting continuous numeric values. They can be applied to cl assification problems by employing a standard method of transforming a classification problem into a problem of function approximation. Surp risingly, using this simple transformation the model tree inducer M5', based on Quinlan's M5, generates more accurate classifiers than the s tate-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.