Summary of Enumerating the K-fold Configurations in Multi-class Classification Problems, by Attila Fazekas and Gyorgy Kovacs
Enumerating the k-fold configurations in multi-class classification problems
by Attila Fazekas, Gyorgy Kovacs
First submitted to arxiv on: 24 Jan 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 the issue of irreproducibility in reported classifier performance scores using k-fold cross-validation. The reproducibility crisis in artificial intelligence is partly attributed to this problem. To tackle it, the authors introduced numerical techniques for testing the consistency of claimed performance scores and experimental setups. A crucial use case involves combinatorial enumeration of all k-fold configurations, which was addressed by proposing an algorithm for binary classification. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary K-fold cross-validation helps measure how well a classifier works. But sometimes, people get different results when they try to repeat the experiment. This can be frustrating and make it hard to trust reported results. To fix this, researchers developed special tools to check if claimed performance scores match the actual experimental setup. One important part of this process is counting all possible combinations of k-fold configurations. The authors came up with a way to do this for simple classification problems. |
Keywords
* Artificial intelligence * Classification
