Summary of Good Practices For Evaluation Of Machine Learning Systems, by Luciana Ferrer et al.
Good practices for evaluation of machine learning systems
by Luciana Ferrer, Odette Scharenborg, Tom Bäckström
First submitted to arxiv on: 4 Dec 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 highlights the crucial role of evaluation procedure design in machine learning (ML) experiments. By carefully selecting training data, features, model architecture, hyperparameters, and test data, developers can influence experiment results. However, it’s argued that the most critical decisions involve designing a reliable evaluation procedure to ensure conclusions generalize to unseen data and are relevant to the application of interest. The wrong evaluation metrics or incorrectly selected data can lead to misleading conclusions or suboptimal development decisions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how to make sure machine learning experiments give us good results. It’s like making sure we’re using the right recipe to get the best outcome. If we don’t choose the right ingredients, cooking time, or method, our results will be wrong. In ML, we need to carefully think about what data we use to train and test our models, as well as which metrics to measure success by. This way, we can trust that our conclusions will work in real-life situations. |
Keywords
» Artificial intelligence » Machine learning
