Variable Grouping Based Bayesian Additive Regression Tree

TL;DR

This paper introduces a variable grouping approach within Bayesian additive regression trees to improve prediction accuracy by modeling nonlinear interactions more efficiently, demonstrated through synthetic and real data experiments.

Contribution

It proposes a novel variable grouping method for BART, enhancing nonlinear interaction modeling and predictive performance with a two-stage approach and available Python package.

Findings

Significant performance improvement over classical methods.

Effective identification of variable groups with no cross-group interactions.

Validated on both synthetic and real datasets.

Abstract

Using ensemble methods for regression has been a large success in obtaining high-accuracy prediction. Examples are Bagging, Random forest, Boosting, BART (Bayesian additive regression tree), and their variants. In this paper, we propose a new perspective named variable grouping to enhance the predictive performance. The main idea is to seek for potential grouping of variables in such way that there is no nonlinear interaction term between variables of different groups. Given a sum-of-learner model, each learner will only be responsible for one group of variables, which would be more efficient in modeling nonlinear interactions. We propose a two-stage method named variable grouping based Bayesian additive regression tree (GBART) with a well-developed python package gbart available. The first stage is to search for potential interactions and an appropriate grouping of variables. The second stage is to build a final model based on the discovered groups. Experiments on synthetic and real data show that the proposed method can perform significantly better than classical approaches.