In this paper we introduce a new machine learning (ML) model for nonlinear regression called Boosting Smooth Transition Regression Tree (BooST). The main advantage of the BooST  is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation than other tree based models concerning the mapping between the covariates and the dependent variable  . We provide some asymptotic theory that shows consistency of the partial derivatives and we present some examples on simulated and empirical data.
In this paper we introduce a new machine learning (ML) model for nonlinear regression called Boosting Smooth Transition Regression Trees (BooST). The main advantage of the BooST model is that it estimates the derivatives (partial effects) of very general nonlinear models, providing more interpretation about the mapping between the covariates and the dependent variable than other tree based models, such as Random Forests . We provide some asymptotic theory that shows consistency of the partial derivative estimates and we present some examples on both simulated and real data.
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Tree
Trees
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model
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than other tree based models concerning
about
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than other tree based models, such as Random Forests
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derivatives
derivative estimates
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simulated and empirical
both simulated and real