A Review of the Receiver Operating Characteristic Curve and a Proof About the Area Beneath It
Fuente:
arXiv
Saved in:
| Main Author: | |
|---|---|
| Format: | Preprint |
| Published: |
2026
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866910184780070912 |
|---|---|
| author | Redolfi, Steven |
| author_facet | Redolfi, Steven |
| contents | The Receiver Operating Characteristic (ROC) curve of a binary classifier has often been utilized to measure the performance of the classifier. The area beneath this curve is used in particular because of its quoted probabilistic interpretation as being equal to the probability that the classifier will rank a random positive observation above a random negative observation. This paper formalizes this claim, produces a bound on how far away from the truth it is if a hypothesis is not met, and gives a small literature review of the ROC curve. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_00926 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Review of the Receiver Operating Characteristic Curve and a Proof About the Area Beneath It Redolfi, Steven Machine Learning Probability The Receiver Operating Characteristic (ROC) curve of a binary classifier has often been utilized to measure the performance of the classifier. The area beneath this curve is used in particular because of its quoted probabilistic interpretation as being equal to the probability that the classifier will rank a random positive observation above a random negative observation. This paper formalizes this claim, produces a bound on how far away from the truth it is if a hypothesis is not met, and gives a small literature review of the ROC curve. |
| title | A Review of the Receiver Operating Characteristic Curve and a Proof About the Area Beneath It |
| topic | Machine Learning Probability |
| url | https://arxiv.org/abs/2605.00926 |