Summary of The Voros: Lifting Roc Curves to 3d, by Christopher Ratigan and Lenore Cowen
The VOROS: Lifting ROC curves to 3D
by Christopher Ratigan, Lenore Cowen
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Metric Geometry (math.MG); Statistics Theory (math.ST); 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 The paper introduces a new approach to evaluating binary classifiers by lifting the traditional Receiver Operating Characteristic (ROC) curve to 3D. The authors propose a Volume Over ROC Surface (VOROS) metric that captures the benefits of different classifiers when there is class imbalance or misclassification costs are unbalanced. This allows for more accurate ranking and comparison of classifier performance in scenarios where exact values are not available, but only ranges are known. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new way to compare binary classifiers by using a 3D surface instead of the usual ROC curve. This helps when some classes have many more examples than others, or when there’s different cost for misclassifying each class. The authors call this the Volume Over ROC Surface (VOROS) and think it’s a better way to rank classifier performance in these situations. |
Keywords
* Artificial intelligence * Roc curve
