  Scaling regression to large datasets is a common problem in many application
areas. We propose a two step approach to scaling regression to large datasets.
Using a regression tree (CART) to segment the large dataset constitutes the
first step of this approach. The second step of this approach is to develop a
suitable regression model for each segment. Since segment sizes are not very
large, we have the ability to apply sophisticated regression techniques if
required. A nice feature of this two step approach is that it can yield models
that have good explanatory power as well as good predictive performance.
Ensemble methods like Gradient Boosted Trees can offer excellent predictive
performance but may not provide interpretable models. In the experiments
reported in this study, we found that the predictive performance of the
proposed approach matched the predictive performance of Gradient Boosted Trees.
