BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions

TL;DR

BooST is a new machine learning model combining boosting with smooth transition trees, enabling estimation of derivatives for better interpretation of nonlinear regressions.

Contribution

It introduces BooST, a novel model that estimates partial effects in nonlinear regression, enhancing interpretability over existing tree-based methods.

Findings

Effective in estimating derivatives in nonlinear models

Outperforms traditional tree methods in interpretability

Validated on simulated and real datasets

Abstract

In this paper, we introduce a new machine learning (ML) model for nonlinear regression called the Boosted Smooth Transition Regression Trees (BooST), which is a combination of boosting algorithms with smooth transition regression trees. The main advantage of the BooST model is the estimation of the derivatives (partial effects) of very general nonlinear models. Therefore, the model can provide more interpretation about the mapping between the covariates and the dependent variable than other tree-based models, such as Random Forests. We present several examples with both simulated and real data.