James Ming Chen (Michigan State University – College of Law) has posted “Linear Beta Coefficients Alongside Feature Importance in Machine Learning” on SSRN. Here is the abstract:
Machine-learning regression models lack the interpretability of their conventional linear counterparts. Tree- and forest-based models offer feature importances, a vector of probabilities indicating the impact of each predictive variable on a model’s results. This brief note describes how to interpret the beta coefficients of the corresponding linear model so that they may be compared directly to feature importances in machine learning.
AI Law Blawg 