%0 Journal Article %1 FAWCETT2006861 %A Fawcett, Tom %D 2006 %J Pattern Recognition Letters %K %N 8 %P 861-874 %R https://doi.org/10.1016/j.patrec.2005.10.010 %T An introduction to ROC analysis %U https://www.sciencedirect.com/science/article/pii/S016786550500303X %V 27 %X Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.

@article{FAWCETT2006861, abstract = {Receiver operating characteristics (ROC) graphs are useful for organizing classifiers and visualizing their performance. ROC graphs are commonly used in medical decision making, and in recent years have been used increasingly in machine learning and data mining research. Although ROC graphs are apparently simple, there are some common misconceptions and pitfalls when using them in practice. The purpose of this article is to serve as an introduction to ROC graphs and as a guide for using them in research.}, added-at = {2021-06-25T07:03:05.000+0200}, author = {Fawcett, Tom}, biburl = {https://www.bibsonomy.org/bibtex/2044a6a9838ad0752ecb3faae95efd20c/michan}, doi = {https://doi.org/10.1016/j.patrec.2005.10.010}, interhash = {c0a67ba4f0a0aa01a0f56f338b8211d9}, intrahash = {044a6a9838ad0752ecb3faae95efd20c}, issn = {0167-8655}, journal = {Pattern Recognition Letters}, keywords = {}, note = {ROC Analysis in Pattern Recognition}, number = 8, pages = {861-874}, timestamp = {2021-06-25T07:03:05.000+0200}, title = {An introduction to ROC analysis}, url = {https://www.sciencedirect.com/science/article/pii/S016786550500303X}, volume = 27, year = 2006 }