Interpreting Linear Beta Coefficients Alongside Feature Importances in Machine Learning

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.

Keywords: machine learning, feature importances, linear regression, beta coefficients

JEL Classification: C18, C33