Detection Error Tradeoff
Encyclopedia
A detection error tradeoff (DET) graph is a graphical plot of error rates for binary classification systems, plotting false reject rate vs. false accept rate. The x- and y-axes are scaled non-linearly by their normal deviates, yielding tradeoff curves that are more linear than ROC curves
, and spend most of the image area highlighting the differences of importance in the critical operating region.
, so that the horizontal axis is x = probit(Pfa) and the vertical is y = probit(Pfr),
where Pfa and Pfr are the false-accept and false-reject rates.
The probit mapping maps probilities which live in the unit interval [0,1], to the extended real line [−∞, +∞]. Since this makes the axes infinitely long, one has to confine the plot to some finite rectangle of interest.
Receiver operating characteristic
In signal detection theory, a receiver operating characteristic , or simply ROC curve, is a graphical plot of the sensitivity, or true positive rate, vs. false positive rate , for a binary classifier system as its discrimination threshold is varied...
, and spend most of the image area highlighting the differences of importance in the critical operating region.
Axis warping
The normal deviate mapping (or normal quantile function, or inverse normal cumulative distribution) is given by the probit functionProbit
In probability theory and statistics, the probit function is the inverse cumulative distribution function , or quantile function associated with the standard normal distribution...
, so that the horizontal axis is x = probit(Pfa) and the vertical is y = probit(Pfr),
where Pfa and Pfr are the false-accept and false-reject rates.
The probit mapping maps probilities which live in the unit interval [0,1], to the extended real line [−∞, +∞]. Since this makes the axes infinitely long, one has to confine the plot to some finite rectangle of interest.
External links
See also
- Receiver operating characteristicReceiver operating characteristicIn signal detection theory, a receiver operating characteristic , or simply ROC curve, is a graphical plot of the sensitivity, or true positive rate, vs. false positive rate , for a binary classifier system as its discrimination threshold is varied...
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