Summary of How Flawed Is Ece? An Analysis Via Logit Smoothing, by Muthu Chidambaram et al.
How Flawed Is ECE? An Analysis via Logit Smoothing
by Muthu Chidambaram, Holden Lee, Colin McSwiggen, Semon Rezchikov
First submitted to arxiv on: 15 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Probability (math.PR)
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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 In this paper, researchers address a fundamental issue in machine learning model calibration. Specifically, they focus on expected calibration error (ECE), a widely used metric to evaluate a model’s predictive accuracy based on its confidence scores. Recent studies have highlighted drawbacks of ECE, such as discontinuities in the space of predictors. The authors investigate these issues and their impacts on existing results, leading to the development of a novel continuous and easily estimated miscalibration metric called Logit-Smoothed ECE (LS-ECE). Initial experiments demonstrate that LS-ECE closely tracks ECE for pre-trained image classification models, suggesting that theoretical pathologies of ECE may be avoided in practice. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Calibrating machine learning models is crucial to ensure their predictions are accurate. Researchers have been using the expected calibration error (ECE) to measure how well a model’s predictions match its confidence levels. However, recent studies have shown some issues with ECE. This paper explores these problems and proposes a new way to evaluate model calibration called Logit-Smoothed ECE (LS-ECE). The authors show that LS-ECE is more continuous and easy to use than ECE. |
Keywords
* Artificial intelligence * Image classification * Machine learning
