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Shapiq: Shapley Interactions for Machine Learning

MCML Authors

Link to Profile Eyke Hüllermeier PI Matchmaking

Eyke Hüllermeier

Prof. Dr.

Principal Investigator

Artificial Intelligence and Machine Learning

Abstract

Originally rooted in game theory, the Shapley Value (SV) has recently become an important tool in machine learning research. Perhaps most notably, it is used for feature attribution and data valuation in explainable artificial intelligence. Shapley Interactions (SIs) naturally extend the SV and address its limitations by assigning joint contributions to groups of entities, which enhance understanding of black box machine learning models. Due to the exponential complexity of computing SVs and SIs, various methods have been proposed that exploit structural assumptions or yield probabilistic estimates given limited resources. In this work, we introduce shapiq, an open-source Python package that unifies state-of-the-art algorithms to efficiently compute SVs and any-order SIs in an application-agnostic framework. Moreover, it includes a benchmarking suite containing 11 machine learning applications of SIs with pre-computed games and ground-truth values to systematically assess computational performance across domains. For practitioners, shapiq is able to explain and visualize any-order feature interactions in predictions of models, including vision transformers, language models, as well as XGBoost and LightGBM with TreeSHAP-IQ. With shapiq, we extend shap beyond feature attributions and consolidate the application of SVs and SIs in machine learning that facilitates future research.

inproceedings

NeurIPS 2024

38th Conference on Neural Information Processing Systems. Vancouver, Canada, Dec 10-15, 2024.
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A* Conference

Authors

M. Muschalik • H. Baniecki • F. Fumagalli • P. Kolpaczki • B. Hammer • E. Hüllermeier

Links

URL GitHub

Research Area

 A3 | Computational Models

BibTeXKey: MBF+24

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