Summary of Are Linear Regression Models White Box and Interpretable?, by Ahmed M Salih and Yuhe Wang
Are Linear Regression Models White Box and Interpretable?
by Ahmed M Salih, Yuhe Wang
First submitted to arxiv on: 16 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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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 discusses the limitations of existing explainable artificial intelligence (XAI) methods, particularly in the context of linear regression models. While traditional XAI metrics suggest that simple models like linear regression are easily interpretable, this perception is challenged by the authors. They argue that even simple models can be complex and difficult to understand when considering factors such as linearity, local explanation, multicollinearity, covariates, normalization, uncertainty, features contribution, and fairness. The paper recommends treating both simple and complex models equally when it comes to explainability and interpretability, highlighting the need for a more nuanced approach to XAI. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Artificial intelligence (AI) is becoming more powerful, but we don’t always understand how it works. This paper looks at how well we can understand simple AI models like linear regression. While some people think these models are easy to understand because they’re simple, the authors disagree. They show that even simple models can be tricky to interpret when you consider all the different factors involved. The authors recommend treating both simple and complex AI models equally when trying to understand how they work. |
Keywords
* Artificial intelligence * Linear regression
