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

Yuri Fonseca; Marcelo Medeiros; Gabriel Vasconcelos; Alvaro Veiga

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arXiv · 1808.03698

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

Yuri Fonseca·Marcelo Medeiros·Gabriel Vasconcelos·Alvaro Veiga

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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.

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Yuri Fonseca, Marcelo Medeiros, Gabriel Vasconcelos, Alvaro Veiga. 2018-08-10. BooST: Boosting Smooth Trees for Partial Effect Estimation in Nonlinear Regressions. https://arxiv.org/abs/1808.03698

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