Summary of Shapiq: Shapley Interactions For Machine Learning, by Maximilian Muschalik et al.
shapiq: Shapley Interactions for Machine Learning
by Maximilian Muschalik, Hubert Baniecki, Fabian Fumagalli, Patrick Kolpaczki, Barbara Hammer, Eyke Hüllermeier
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The Shapley Value (SV) has become a crucial tool in machine learning research, particularly for feature attribution and data valuation in explainable artificial intelligence. Shapley Interactions (SIs) naturally extend the SV by assigning joint contributions to groups of entities, enhancing understanding of black box machine learning models. Despite limitations, various methods have been proposed that exploit structural assumptions or yield probabilistic estimates given limited resources. This work introduces 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. The package includes a benchmarking suite containing 11 machine learning applications with pre-computed games and ground-truth values to systematically assess computational performance across domains. SIs can be used for feature attribution and data valuation, and the shapiq package provides a way to efficiently compute SVs and SIs. This is useful for practitioners who want to explain and visualize any-order feature interactions in predictions of models, including vision transformers, language models, XGBoost, and LightGBM with TreeSHAP-IQ. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Shapley Value (SV) is a tool used in machine learning to understand how different parts of a model work together. It helps us figure out what’s important and what’s not. This new package called shapiq makes it easier to use SVs and something called Shapley Interactions (SIs). SIs are like groups of things that work together, and they help us understand complex models better. The package has lots of examples and tests so we can compare how well it works on different kinds of problems. This is important because SVs and SIs can be used to explain how machine learning models make predictions. It’s like being able to see behind the curtain and saying “aha! I get it now!” |
Keywords
» Artificial intelligence » Machine learning » Xgboost
