Summary of Statistical Significance Of Feature Importance Rankings, by Jeremy Goldwasser and Giles Hooker
Statistical Significance of Feature Importance Rankings
by Jeremy Goldwasser, Giles Hooker
First submitted to arxiv on: 28 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG)
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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 This paper addresses the issue of instability in feature importance scores, a crucial tool for understanding machine learning model predictions. The authors propose innovative methods that guarantee high-probability correctness of the most important features, including their ranking order. By leveraging hypothesis testing ideas, they develop techniques to retrospectively verify the stability of top-ranked features and introduce efficient sampling algorithms to identify the K most important features with probability exceeding 1-α. The authors demonstrate the effectiveness of these methods on popular attribution tools like SHAP and LIME. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us better understand how machine learning models work by making sure we get the right answers about which features are most important. Right now, many tools used to do this can give different results because they use random sampling. The authors came up with new ways to solve this problem, using ideas from statistical testing. They show how to check if the top-ranked features are correct and develop efficient algorithms to find the most important features. |
