Summary of Model Assessment and Selection Under Temporal Distribution Shift, by Elise Han et al.
Model Assessment and Selection under Temporal Distribution Shift
by Elise Han, Chengpiao Huang, Kaizheng Wang
First submitted to arxiv on: 13 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Methodology (stat.ME)
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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 proposes an adaptive approach for assessing and selecting machine learning models in situations where the training data may not be representative of the target environment. To address this issue, the authors develop a rolling window method that estimates the generalization error of a model and allows for comparisons between different models. The proposed approach is tested through numerical experiments and theoretical analyses, demonstrating its adaptivity to non-stationary data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about finding the best machine learning model when the training data might not be like the real-world situation. It’s trying to solve this problem by creating a way to test models in different situations and compare them. This will help us choose the best model for a specific job, even if it has never seen that type of data before. |
Keywords
* Artificial intelligence * Generalization * Machine learning
