In signal detection theory, a<unk>  <unk> , or simply<unk>  curve, is a graphical plot of the sensitivity vs. (1 -  specificity) for a binary classifier system as its discrimination threshold is varied.  The<unk>  can also be represented equivalently by plotting the fraction of true positives (TPR = true positive rate) vs. the fraction of false positives (FPR = false positive rate). Also known as a Relative<unk><unk>  curve, because it is a comparison of two<unk><unk> s (TPR & FPR) as the criterion changes (Signal detection theory and<unk>  analysis in psychology and diagnostics : collected papers; Swets, 1996).<unk>  analysis provides tools to select possibly optimal models and to discard suboptimal ones independently from (and prior to specifying) the cost context or the class distribution.<unk>  analysis is related in a direct and natural way to cost/benefit analysis of diagnostic decision making. Widely used in medicine, radiology, psychology and other areas for many decades, it has been introduced relatively recently in other areas like machine learning and data mining.
Receiver operating characteristic
Linear regression
Ancillary statistic
Cohen's kappa
Proportional hazards models
Cumulative distribution function
Box plot
Propensity score