  Bayesian Additive Regression Trees (BART) is a tree-based machine learning
method that has been successfully applied to regression and classification
problems. BART assumes regularisation priors on a set of trees that work as
weak learners and is very flexible for predicting in the presence of
non-linearity and high-order interactions. In this paper, we introduce an
extension of BART, called Model Trees BART (MOTR-BART), that considers
piecewise linear functions at node levels instead of piecewise constants. In
MOTR-BART, rather than having a unique value at node level for the prediction,
a linear predictor is estimated considering the covariates that have been used
as the split variables in the corresponding tree. In our approach, local
linearities are captured more efficiently and fewer trees are required to
achieve equal or better performance than BART. Via simulation studies and real
data applications, we compare MOTR-BART to its main competitors. R code for
MOTR-BART implementation is available at https://github.com/ebprado/MOTR-BART.
