Summary of Alternative Methods to Shap Derived From Properties Of Kernels: a Note on Theoretical Analysis, by Kazuhiro Hiraki et al.
Alternative Methods to SHAP Derived from Properties of Kernels: A Note on Theoretical Analysis
by Kazuhiro Hiraki, Shinichi Ishihara, Junnosuke Shino
First submitted to arxiv on: 1 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper derives an analytical expression for AFA, a technique for explaining AI models, using LIME’s kernel. It then proposes new AFAs with desirable properties and compares them to existing methods like SHAP. This research aims to improve our understanding of how AFAs work and how they can be applied in various domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study helps us understand how AI models make decisions by creating a math formula for AFA, which is a way to explain why a model makes certain predictions. The researchers also introduce new ways of calculating AFA that have special properties or match a concept from game theory. They compare these new methods to existing ones like SHAP. |
