KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions

Fumagalli, Fabian; Muschalik, Maximilian ; Kolpaczki, Patrick; HÜllermeier, Eyke und Hammer, Barbara (July 2024): KernelSHAP-IQ: Weighted Least Square Optimization for Shapley Interactions. 41st International Conference on Machine Learning (ICML 2024), Vienna, Austria, 21. - 27. July 2024. In: Proceedings of the 41st International Conference on Machine Learning, Proceedings of Machine Learning Research Vol. 235 PMLR. pp. 14308-14342

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.

• Item Type: Conference or Workshop Item (Paper)
• Faculties: Mathematics, Computer Science and Statistics > Computer Science > Artificial Intelligence and Machine Learning
• Subjects: 000 Computer science, information and general works > 000 Computer science, knowledge, and systems
• ISSN: 2640-3498
• Language: English
• DFG: Gefördert durch die Deutsche Forschungsgemeinschaft (DFG) - 438445824