Using Model Trees for Classiication

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 classiication problems by employing a standard method of transforming a classiication problem into a problem of function approximation. Surprisingly, using this simple transformation the model tree inducer M5 0 , based on Quinlan's M5, generates more accurate clas-siiers than the state-of-the-art decision tree learner C5.0, particularly when most of the attributes are numeric.