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

arXiv: https://arxiv.org/abs/1808.03698 · HTML: https://mlsanity.com/paper/1808.03698/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Aug 2018.

## Built on by (most cited first, 1 of 1)

- [Managers versus Machines: Do Algorithms Replicate Human Intuition in Credit Ratings?](https://mlsanity.com/paper/2202.04218/) (2022)

## Abstract (arXiv)

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.