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.