  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.
