A nonparametric function estimation method called SUPPORT (“Smoothed and Unsmoothed Piecewise-Polynomial Regression Trees”) is described. The estimate is typically made up of several pieces, each piece being obtained by fitting a polynomial regression to the observations in a subregion of the data space. Partitioning is carried out recursively as in a tree-structured method. If the estimate is ...

This paper presents a novel method for learning in domains with continuous target variables. The method integrates regression trees with kernel regression models. The integration is done by adding kernel regressors at the tree leaves producing what we call kernel regression trees. The approach is motivated by the goal of trying to take advantage of the different biases of the two regression met...

We develop a Bayesian “sum-of-trees” model where each tree is constrained by a regularization prior to be a weak learner, and fitting and inference are accomplished via an iterative Bayesian backfitting MCMC algorithm that generates samples from a posterior. Effectively, BART is a nonparametric Bayesian regression approach which uses dimensionally adaptive random basis elements. Motivated by en...