an introduction to roc analysis

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An introduction to ROC analysis

1 Introduction A receiver operating characteristics (ROC) graph is a technique for visualizing organizing and selecting classifi-ers based on their performance ROC graphs have long been used in signal detection theory to depict the tradeoff between hit rates and false alarm rates of classifiers (Egan 1975; Swets et al 2000)

What is ROC analysis?
ROC analysis is commonly employed in medical decision making in which two-class diagnostic problems—presence or absence of an abnormal condition—are common. The two axes represent tradeoffs between errors (false positives) and benefits (true positives) that a classifier makes between two classes.
What are receiver operating characteristics (ROC) graphs used for?
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.
What is a ROC plot?
The ROC plot provides a visual representation of the accuracy of a detection test, incorporating not only the intrinsic features of the test, but also reader variability. shows an example of how ROC curves were used to analyze the value of a computer-assisted detection (CAD) system.
Can ROC graphs be used in research?
ROC graphs are conceptually simple, but there are some non-obvious complexities that arise when they are used in research. There are also common misconceptions and pit-falls when using them in practice. This article attempts to serve as a basic introduction to ROC graphs and as a guide for using them in research.

ROC Curves and Area Under the Curve (AUC) Explained

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