Model Interpretation: A Unified Derivative-based Framework for Nonparametric Regression and Supervised Machine Learning

Understand

Interpreting a nonparametric regression model with many predictors is known to be a challenging problem.

• There has been renewed interest in this topic due to the extensive use of machine learning algorithms and the difficulty in understanding and explaining their input-output relationships.
• This paper develops a unified framework using a derivative-based approach for existing tools in the literature, including the partial-dependence plots, marginal plots and accumulated effects plots.
• It proposes a new interpretation technique called the accumulated total derivative effects plot and demonstrates how its components can be used to develop extensive insights in complex regression models with correlated predictors.