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[sixty four][sixty five] To ordinary about all pairwise classes, one computes the AUC for every set of classes, working with only the examples from those two classes like there have been no other classes, after which you can averages these AUC values above all probable pairs. When there are actually c classes there'll be c(c − one) / 2 achievable pairs of classes.

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Since TPR is similar to sensitivity and FPR is equivalent to one − specificity, the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot. Each prediction outcome or instance of the confusion matrix represents read more 1 position in the ROC Place.

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ROC curves also proved helpful for your evaluation of machine learning methods. The initial application of ROC in machine learning was by Spackman who demonstrated the worth of ROC curves in comparing and analyzing different classification algorithms.[61]

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