Summary of Double Descent: Understanding Linear Model Estimation Of Nonidentifiable Parameters and a Model For Overfitting, by Ronald Christensen
Double Descent: Understanding Linear Model Estimation of Nonidentifiable Parameters and a Model for Overfitting
by Ronald Christensen
First submitted to arxiv on: 23 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Statistics Theory (math.ST)
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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 explores various methods for estimating parameters in cases where the number of features (p) exceeds the number of samples (n). The authors examine ordinary least squares estimation, as well as its variations such as penalized least squares and spectral shrinkage estimates. They also discuss prediction methods for new observations and introduce notational changes to facilitate discussions on overfitting. The paper concludes by illustrating the phenomenon of double descent. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding the best way to guess what’s going on when you have more features than data points. The authors look at different methods, like regularized least squares, to help solve this problem. They also talk about predicting new results and how to avoid overfitting. The main idea is that there are many ways to approach this challenge, and the authors want to show which ones work best. |
Keywords
* Artificial intelligence * Overfitting
