Summary of Robustness Investigation Of Cross-validation Based Quality Measures For Model Assessment, by Thomas Most et al.
Robustness investigation of cross-validation based quality measures for model assessment
by Thomas Most, Lars Gräning, Sebastian Wolff
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: 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 paper explores the accuracy and robustness of quality measures for assessing machine learning models. It proposes a model-independent evaluation approach using cross-validation to estimate the approximation error on unknown data. The study presents measures that quantify explained variation in model predictions, and assesses their reliability through numerical examples with an additional verification dataset. Furthermore, it estimates confidence bounds and derives local quality measures from prediction residuals. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper investigates how well machine learning models work and if they’re reliable. It comes up with a way to check model quality that doesn’t rely on the specific model itself. Instead, it uses a technique called cross-validation to see how well the model does on data it hasn’t seen before. The study shows that this method can give us accurate measures of model performance, and even provides ways to estimate how confident we should be in those measurements. |
Keywords
» Artificial intelligence » Machine learning
