Summary of Overcoming Common Flaws in the Evaluation Of Selective Classification Systems, by Jeremias Traub et al.
Overcoming Common Flaws in the Evaluation of Selective Classification Systems
by Jeremias Traub, Till J. Bungert, Carsten T. Lüth, Michael Baumgartner, Klaus H. Maier-Hein, Lena Maier-Hein, Paul F Jaeger
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV); Methodology (stat.ME)
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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 Machine learning educators can now confidently introduce their students to selective classification, a technique that allows models to reject low-confidence predictions. This approach is crucial for reliable translation to real-world scenarios like clinical diagnostics. However, current evaluation methods assume fixed working points based on pre-defined rejection thresholds. To bridge this gap, researchers propose the Area under the Generalized Risk Coverage curve (AUGRC), which meets five essential requirements: task alignment, interpretability, and flexibility. This metric can be directly interpreted as the average risk of undetected failures. Empirical demonstrations on six datasets and 13 confidence scoring functions show that AUGRC substantially changes metric rankings on five out of the six data sets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Machine learning is a powerful tool for classifying things like medical images or texts. But what if the model isn’t sure about its prediction? That’s where selective classification comes in – it lets models reject low-confidence predictions, making it more reliable in real-world scenarios. Current methods assume certain rules for when to accept or reject predictions, but this paper shows that these rules don’t always work. Instead, they propose a new metric called AUGRC that can directly show the risk of missing something important. They tested this on six different datasets and found that it changed how we ranked models in five out of the six cases. |
Keywords
» Artificial intelligence » Alignment » Classification » Machine learning » Translation
