Provably Stable Feature Rankings with SHAP and LIME
Jeremy Goldwasser Giles Hooker
Feature attributions are ubiquitous tools for understanding the predictions
of machine learning models. However, popular methods for scoring input
variables such as SHAP and LIME suffer from high instability due to random
sampling. Leveraging ideas from multiple hypothesis testing, we devise
attribution methods that correctly rank the most important features with high
probability. Our algorithm RankSHAP guarantees that the $K$ highest Shapley
values have the proper ordering with probability exceeding $1-\alpha$.
Empirical results demonstrate its validity and impressive computational
efficiency. We also build on previous work to yield similar results for LIME,
ensuring the most important features are selected in the right order.