Partially fixed bayesian additive regression trees

Abstract

Bayesian Additive Regression Trees (BART) is a widely popular nonparametric regression model known for its accurate prediction capabilities. In certain situations, there is knowledge suggesting the existence of certain dominant variables. However, the BART model fails to fully utilize the knowledge. To tackle this problem, the paper introduces a modification to BART known as the Partially Fixed BART model. By fixing a portion of the trees' structure, this model enables more efficient utilization of prior knowledge, resulting in enhanced estimation accuracy. Moreover, the Partially Fixed BART model can offer more precise estimates and valuable insights for future analysis even when such prior knowledge is absent. Empirical results substantiate the enhancement of the proposed model in comparison to the original BART.

Keywords

• Bayesian additive regression trees
• nonparametric model
• machine learning
• variable importance