Summary of Soak: Same/other/all K-fold Cross-validation For Estimating Similarity Of Patterns in Data Subsets, by Toby Dylan Hocking et al.
SOAK: Same/Other/All K-fold cross-validation for estimating similarity of patterns in data subsets
by Toby Dylan Hocking, Gabrielle Thibault, Cameron Scott Bodine, Paul Nelson Arellano, Alexander F Shenkin, Olivia Jasmine Lindly
First submitted to arxiv on: 11 Oct 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Artificial Intelligence (cs.AI); 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 The proposed SOAK method enables the estimation of learnable/predictable patterns in data subsets by comparing models trained on different subsets and evaluating their performance on a fixed test subset. This approach can be used to determine whether it is beneficial to combine subsets during model training or not. The authors demonstrate the effectiveness of SOAK on six real-world datasets, three image pair datasets, and 11 benchmark datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary SOAK is a new way to check if predictions will be accurate when using data from different places or times. It also helps decide if combining different subsets of data during model training makes sense. To test this, the authors used six real-world datasets with different geographic or time-based subsets and three image pair datasets with different types of images. They also tested SOAK on 11 benchmark datasets that have predefined train-test splits. |
