  Supervised Machine Learning (SML) algorithms, such as Gradient Boosting,
Random Forest, and Neural Networks, have become popular in recent years due to
their superior predictive performance over traditional statistical methods.
However, their complexity makes the results hard to interpret without
additional tools. There has been a lot of recent work in developing global and
local diagnostics for interpreting SML models. In this paper, we propose a
locally-interpretable model that takes the fitted ML response surface,
partitions the predictor space using model-based regression trees, and fits
interpretable main-effects models at each of the nodes. We adapt the algorithm
to be efficient in dealing with high-dimensional predictors. While the main
focus is on interpretability, the resulting surrogate model also has reasonably
good predictive performance.
