Summary of Is K-fold Cross Validation the Best Model Selection Method For Machine Learning?, by Juan M Gorriz et al.
Is K-fold cross validation the best model selection method for Machine Learning?
by Juan M Gorriz, R. Martin Clemente, F Segovia, J Ramirez, A Ortiz, J. Suckling
First submitted to arxiv on: 29 Jan 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Machine Learning (cs.LG); Image and Video Processing (eess.IV); Signal Processing (eess.SP)
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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 Machine learning has significant potential for predictive inference, but assessing the likelihood of an outcome being generated by chance can be challenging. K-fold cross-validation (CV) is a common approach that often outperforms conventional hypothesis testing. By using measures like accuracy directly obtained from machine learning classifications, CV improves the likelihood assessment. To incorporate frequentist analysis into machine learning pipelines, permutation tests or simple statistics from data partitions can be used to estimate confidence intervals. However, small sample-size datasets and heterogeneous data sources still pose challenges. To address these issues, a novel statistical test called K-fold CUBV (Upper Bound of the actual risk) is proposed. This test uses concentration inequalities to bound uncertain predictions with CV. Results on simulated and neuroimaging datasets show that K-fold CUBV is a robust criterion for detecting effects and validating accuracy values. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is a powerful tool, but it’s hard to know if the results are real or just by chance. One way to check is called k-fold cross-validation (CV). CV looks at how well the machine learning model works on different parts of the data and averages the results. This helps us figure out if the results are reliable. But there’s a problem – sometimes we don’t have enough data, or the data is from different places. To fix this, researchers came up with a new way to test the results using CV. They called it K-fold CUBV (Upper Bound of the actual risk). It helps us know if the results are real or not. The tests showed that K-fold CUBV works well on both computer simulations and real-life data from brain imaging. |
Keywords
* Artificial intelligence * Inference * Likelihood * Machine learning
