Shapley variable importance cloud for interpretable machine learning.

ABSTRACT: Interpretable machine learning has been focusing on explaining final models that optimize performance. The state-of-the-art Shapley additive explanations (SHAP) locally explains the variable impact on individual predictions and has recently been extended to provide global assessments across the dataset. Our work further extends "global" assessments to a set of models that are "good enough" and are practically as relevant as the final model to a prediction task. The resulting Shapley variable importance cloud consists of Shapley-based importance measures from each good model and pools information across models to provide an overall importance measure, with uncertainty explicitly quantified to support formal statistical inference. We developed visualizations to highlight the uncertainty and

SUBMITTER: Ning Y 

PROVIDER: S-EPMC9023900 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature