Harris, C. and Pymar, Richard and Rowat, Colin (2022) Joint Shapley Values: a measure of joint feature importance. International Conference on Learning Representations ,
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Abstract
The Shapley value is one of the most widely used measures of feature importance partly as it measures a feature's average effect on a model's prediction. We introduce joint Shapley values, which directly extend Shapley's axioms and intuitions: joint Shapley values measure a set of features' average effect on a model's prediction. We prove the uniqueness of joint Shapley values, for any order of explanation. Results for games show that joint Shapley values present different insights from existing interaction indices, which assess the effect of a feature within a set of features. The joint Shapley values seem to provide sensible results in ML attribution problems. With binary features, we present a presence-adjusted global value that is more consistent with local intuitions than the usual approach.
Metadata
| Item Type: | Article |
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| Additional Information: | The Tenth International Conference on Learning Representations, ICLR 2022 |
| School: | Birkbeck Faculties and Schools > Faculty of Science > School of Computing and Mathematical Sciences |
| Depositing User: | Richard Pymar |
| Date Deposited: | 18 Mar 2022 06:15 |
| Last Modified: | 11 Apr 2025 10:59 |
| URI: | https://eprints.bbk.ac.uk/id/eprint/47310 |
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