A receiver operating characteristics (ROC) graph is a

technique for visualizing, organizing and selecting classifiers 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). ROC analysis has been extended

for use in visualizing and analyzing the behavior of diagnostic systems (Swets, 1988). The medical decision making

community has an extensive literature on the use of ROC

graphs for diagnostic testing (Zou, 2002). Swets et al.

(2000) brought ROC curves to the attention of the wider

public with their Scientific American article.

One of the earliest adopters of ROC graphs in machine

learning was Spackman (1989), who demonstrated the

value of ROC curves in evaluating and comparing algorithms. Recent years have seen an increase in the use of

ROC graphs in the machine learning community, due in

part to the realization that simple classification accuracy

is often a poor metric for measuring performance (Provost

and Fawcett, 1997; Provost et al., 1998). In addition to

being a generally useful performance graphing method,

they have properties that make them especially useful for

domains with skewed class distribution and unequal classification error costs. These characteristics have become

increasingly important as research continues into the areas

of cost-sensitive learning and learning in the presence of

unbalanced classes.

For more details see the file  :

https://ccrma.stanford.edu/workshops/mir2009/references/ROCintro.pdf

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