Summary of Reassessing How to Compare and Improve the Calibration Of Machine Learning Models, by Muthu Chidambaram and Rong Ge
Reassessing How to Compare and Improve the Calibration of Machine Learning Models
by Muthu Chidambaram, Rong Ge
First submitted to arxiv on: 6 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Statistics Theory (math.ST); Machine Learning (stat.ML)
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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 A machine learning model is considered calibrated if its predicted probability matches the observed frequency conditional on the prediction. This property has become crucial as ML models impact various domains. Despite many recent papers on calibration, we reassess reporting of calibration metrics in the literature. We show that trivial recalibration approaches can appear state-of-the-art without considering test accuracy and additional generalization metrics like negative log-likelihood. Our work develops a new extension to reliability diagrams that jointly visualizes calibration and generalization error, helping detect trade-offs between the two. We also prove novel results regarding full and confidence calibration errors for Bregman divergences. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A machine learning model is considered good if its predictions match what actually happens. This is important because ML models are used in many areas like healthcare and finance. Some people have been studying how to make these models better, but we’re not sure they’re doing it right. We looked at how people report their results and found that some simple tricks can make a model look better than it really is. To fix this, we developed a new way to visualize how well a model is doing and show trade-offs between making good predictions and being confident in those predictions. |
Keywords
» Artificial intelligence » Generalization » Log likelihood » Machine learning » Probability
