Summary of A Bayesian Explanation Of Machine Learning Models Based on Modes and Functional Anova, by Quan Long
A Bayesian explanation of machine learning models based on modes and functional ANOVA
by Quan Long
First submitted to arxiv on: 5 Nov 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computation (stat.CO)
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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 proposes an innovative approach to explainable AI, tackling the “inverse explanation problem” where it’s given the deviation of a label, and we need to find the reasons for this deviation. Building upon a Bayesian framework, the authors recover the true features conditioned on the observed label value. By using distances in ANOVA functional decomposition, they efficiently identify and rank the influential features that contributed to the deviation from the mode. This approach is shown to be more human-intuitive and robust compared to methods based on mean values like SHAP values. Furthermore, the proposed method’s extra computational costs are dimension-independent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper takes a new approach to explaining why something didn’t turn out as expected. Instead of looking at why something was predicted correctly, it tries to figure out what went wrong and why. The authors use a special kind of math called Bayesian framework to recover the real features that caused the deviation from the norm. By comparing how far each feature is from the average, they can identify which ones had the most impact on the outcome. This approach is more intuitive and reliable than previous methods and doesn’t get harder with bigger datasets. |
