Abstract
The Shapley value (SV) is a prevalent approach of allocating credit to machine learning (ML) entities to understand black box ML models. Enriching such interpretations with higher-order interactions is inevitable for complex systems, where the Shapley Interaction Index (SII) is a direct axiomatic extension of the SV. While it is well-known that the SV yields an optimal approximation of any game via a weighted least square (WLS) objective, an extension of this result to SII has been a long-standing open problem, which even led to the proposal of an alternative index. In this work, we characterize higher-order SII as a solution to a WLS problem, which constructs an optimal approximation via SII and k-Shapley values (k-SII). We prove this representation for the SV and pairwise SII and give empirically validated conjectures for higher orders. As a result, we propose KernelSHAP-IQ, a direct extension of KernelSHAP for SII, and demonstrate state-of-the-art performance for feature interactions.
Dokumententyp: | Konferenzbeitrag (Paper) |
---|---|
Fakultät: | Mathematik, Informatik und Statistik > Informatik > Künstliche Intelligenz und Maschinelles Lernen |
Themengebiete: | 000 Informatik, Informationswissenschaft, allgemeine Werke > 000 Informatik, Wissen, Systeme |
URN: | urn:nbn:de:bvb:19-epub-121732-3 |
ISSN: | 2640-3498 |
Sprache: | Englisch |
Dokumenten ID: | 121732 |
Datum der Veröffentlichung auf Open Access LMU: | 09. Okt. 2024 09:29 |
Letzte Änderungen: | 04. Dez. 2024 12:36 |
DFG: | Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 438445824 |