ABSTRACT
Regression trees are similar to additive models in that they represent a compromise
between the linear model and the completely nonparametric approach. Tree method-
ology has roots in both the statistics and computer science literature. A precursor
to current methodology was CHAID developed by Morgan and Sonquist (1963) al-
though the book by Breiman, Friedman, Olshen, and Stone (1984) introduced the
main ideas to statistics. Concurrently, tree methodology was developed in machine
learning starting in the 1970s — see Quinlan (1993) for an overview.
