Summary of Don’t Waste Your Time: Early Stopping Cross-validation, by Edward Bergman et al.
Don’t Waste Your Time: Early Stopping Cross-Validation
by Edward Bergman, Lennart Purucker, Frank Hutter
First submitted to arxiv on: 6 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 addresses a common issue in automated machine learning (AutoML) for tabular data: the high computational cost of validating models using k-fold cross-validation. The authors aim to make model selection with cross-validation more efficient by exploring early stopping strategies during the validation process. They investigate the impact of early stopping on random search and Bayesian optimization across 36 classification datasets, considering different numbers of folds (3-, 5-, and 10-folds) and repeated cross-validation. Their results show that a simple and easy-to-implement method consistently allows model selection to converge faster, exploring more configurations while achieving better overall performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make it easier to choose the best machine learning models for tabular data. It’s currently hard because we have to test many different models and see which one works best. The authors want to fix this by stopping the testing process earlier than usual, so we can find a good model faster. They tried this on 36 different datasets and saw that it works well. By stopping early, they were able to look at more options and still get better results. |
Keywords
» Artificial intelligence » Classification » Early stopping » Machine learning » Optimization
