Tree Structured Interpretable Regression

We describe a new method of tree-based regression. The response is estimated by building an adaptive linear model which varies for different paths through the tree. The method has the following potential advantages over traditional methods: the method can naturally be applied to very large datasets in which only a small proportion of the predictors are useful, the resulting regression rules are more easily interpreted and applied, and may be more accurate in application, since the rules are derived by means of a cross-validation technique which maximizes their predictive accuracy. The system is evaluated in an empirical study and compared to traditional regression and CART systems.