VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution
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arXiv
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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866915333157158912 |
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| author | Gajewski, Mateusz Morzy, Mikołaj Karczmarz, Adam Sankowski, Piotr |
| author_facet | Gajewski, Mateusz Morzy, Mikołaj Karczmarz, Adam Sankowski, Piotr |
| contents | Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnostic local feature attribution method which uses the reduction of prediction variance as the key importance metric of features. Building upon Shapley value framework, VARSHAP satisfies the key Shapley axioms, but, unlike SHAP, is resilient to global data distribution shifts. Experiments on synthetic and real-world datasets demonstrate that VARSHAP outperforms popular methods such as KernelSHAP or LIME, both quantitatively and qualitatively. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_07229 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution Gajewski, Mateusz Morzy, Mikołaj Karczmarz, Adam Sankowski, Piotr Machine Learning Existing feature attribution methods like SHAP often suffer from global dependence, failing to capture true local model behavior. This paper introduces VARSHAP, a novel model-agnostic local feature attribution method which uses the reduction of prediction variance as the key importance metric of features. Building upon Shapley value framework, VARSHAP satisfies the key Shapley axioms, but, unlike SHAP, is resilient to global data distribution shifts. Experiments on synthetic and real-world datasets demonstrate that VARSHAP outperforms popular methods such as KernelSHAP or LIME, both quantitatively and qualitatively. |
| title | VARSHAP: Addressing Global Dependency Problems in Explainable AI with Variance-Based Local Feature Attribution |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2506.07229 |