BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions
This work addresses the need for interpretable nonlinear regression models in machine learning, offering a method to estimate partial effects, though it appears incremental as it builds on existing boosting and tree-based techniques.
The paper tackles the problem of estimating partial effects in nonlinear regressions by introducing BooST, a model combining boosting with smooth transition regression trees, which provides more interpretable derivatives than other tree-based models like Random Forests, as demonstrated with simulated and real data examples.
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.