Surrogate Locally-Interpretable Models with Supervised Machine Learning Algorithms
This addresses the interpretability issue for users of complex ML models, but it is incremental as it builds on existing local diagnostic methods.
The paper tackles the problem of interpreting complex supervised machine learning models by proposing a locally-interpretable surrogate model that partitions the predictor space using model-based regression trees and fits interpretable main-effects models at each node, with the resulting model offering reasonably good predictive performance.
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.