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2019-05-12T07:37:53Z

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[[File:ext_38dKJsdjh_Example of DET curves.png|thumb|Two hypothetical classifiers compared via DET curves.]]
[[File:ext_38dKJsdjh_Example ROC curves.png|thumb|The same two classifiers compared via traditional ROC curves.]]
A '''detection error tradeoff''' ('''DET''') '''graph''' is a graphical plot of error rates for [[binary classifier | binary classification]] systems, plotting the false rejection rate vs. false acceptance rate. The x- and y-axes are scaled non-linearly by their [[standard normal deviate]]s (or just by logarithmic transformation), yielding tradeoff curves that are more linear than [[Receiver operating characteristic |ROC curves]], and use most of the image area to highlight 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|probit function]], 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 probabilities from 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.

==See also==

* [[Constant false alarm rate]]
* [[Detection theory]]
* [[False alarm]]
* [[Receiver operating characteristic]]

==Source==

[http://wikipedia.org/ http://wikipedia.org/]
[[Category:Error-correcting codes]]

Detection error tradeoff - Revision history

Admin: Created page with "Two hypothetical classifiers compared via DET curves. File:ext_38dKJsdjh_Example ROC curves.png|thumb|The same two cla..."