Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | https://arxiv.org/abs/2512.07578 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866917455862956032 |
|---|---|
| author | Kim, Dongseok Choi, Hyoungsun Rasool, Mohamed Jismy Aashik Oh, Gisung |
| author_facet | Kim, Dongseok Choi, Hyoungsun Rasool, Mohamed Jismy Aashik Oh, Gisung |
| contents | Global SHAP explanations are typically presented as feature-importance rankings, which identify variables that matter to a black-box model but do not indicate whether their effects admit clear directional summaries, how uncertain those summaries are, or how faithfully they represent the fitted response. This paper proposes the $ϕ$-table, a SHAP-based statistical explanation table for tabular black-box regression models. The procedure selects features by SHAP importance and fits a standardized linear surrogate to the fitted model response $f(X)$, reporting SHAP importance together with model-response coefficients, uncertainty summaries, surrogate fidelity, and bootstrap coefficient stability. The resulting coefficients are interpreted as projections of the fitted model response onto the SHAP-selected feature set. Across synthetic, semi-synthetic, and real-data experiments, the $ϕ$-table extends ranking-only SHAP into a statistical global explanation by exposing direction, uncertainty, fidelity, and stability as distinct components of fitted model behavior. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_07578 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | $ϕ$-Table: A Statistical Explanation for Global SHAP Kim, Dongseok Choi, Hyoungsun Rasool, Mohamed Jismy Aashik Oh, Gisung Machine Learning Methodology Global SHAP explanations are typically presented as feature-importance rankings, which identify variables that matter to a black-box model but do not indicate whether their effects admit clear directional summaries, how uncertain those summaries are, or how faithfully they represent the fitted response. This paper proposes the $ϕ$-table, a SHAP-based statistical explanation table for tabular black-box regression models. The procedure selects features by SHAP importance and fits a standardized linear surrogate to the fitted model response $f(X)$, reporting SHAP importance together with model-response coefficients, uncertainty summaries, surrogate fidelity, and bootstrap coefficient stability. The resulting coefficients are interpreted as projections of the fitted model response onto the SHAP-selected feature set. Across synthetic, semi-synthetic, and real-data experiments, the $ϕ$-table extends ranking-only SHAP into a statistical global explanation by exposing direction, uncertainty, fidelity, and stability as distinct components of fitted model behavior. |
| title | $ϕ$-Table: A Statistical Explanation for Global SHAP |
| topic | Machine Learning Methodology |
| url | https://arxiv.org/abs/2512.07578 |