Generalized SHAP: Generating multiple types of explanations in machine learning
Abstract
Many important questions about a model cannot be answered just by explaining how much each feature contributes to its output. To answer a broader set of questions, we generalize a popular, mathematically well-grounded explanation technique, Shapley Additive Explanations (SHAP). Our new method - Generalized Shapley Additive Explanations (G-SHAP) - produces many additional types of explanations, including: 1) General classification explanations; Why is this sample more likely to belong to one class rather than another? 2) Intergroup differences; Why do our model's predictions differ between groups of observations? 3) Model failure; Why does our model perform poorly on a given sample? We formally define these types of explanations and illustrate their practical use on real data.
Cite
@article{arxiv.2006.07155,
title = {Generalized SHAP: Generating multiple types of explanations in machine learning},
author = {Dillon Bowen and Lyle Ungar},
journal= {arXiv preprint arXiv:2006.07155},
year = {2020}
}
Comments
12 pages, 7 figures. Based on a submission to NeurIPS 2020. Dillon Bowen is credited with the original concept, code, data analysis, and initial paper draft. Lyle Ungar is credited with contributions to the draft and mathematical notation. Documentation can be found at https://dsbowen.github.io/gshap/