Summary of Classical Statistical (in-sample) Intuitions Don’t Generalize Well: a Note on Bias-variance Tradeoffs, Overfitting and Moving From Fixed to Random Designs, by Alicia Curth
Classical Statistical (In-Sample) Intuitions Don’t Generalize Well: A Note on Bias-Variance Tradeoffs, Overfitting and Moving from Fixed to Random Designs
by Alicia Curth
First submitted to arxiv on: 27 Sep 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 reveals that the recent emergence of machine learning phenomena like double descent and benign overfitting can be attributed to a shift from evaluating models based on in-sample prediction error to generalization error. This change, from fixed to random designs, has significant consequences for textbook intuitions regarding the bias-variance tradeoff. The paper highlights how this move affects our understanding of these phenomena, which are often observed as going against classical statistical intuitions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study shows that modern machine learning’s unusual behaviors can be explained by a simple change in evaluation methods. Instead of focusing on how well models predict in-sample data, we now care about their ability to generalize to new, unseen data. This shift from fixed to random designs has big implications for our understanding of bias and variance tradeoffs. By looking at this change, the paper helps us make sense of seemingly counterintuitive ML phenomena. |
Keywords
» Artificial intelligence » Generalization » Machine learning » Overfitting
