The Partial Area Under the ROC Curve (pAUC) is a metric for the performance of binary classifier.
It is computed based on the receiver operating characteristic (ROC) curve that illustrates the diagnostic ability of a given binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.
An example of ROC curve and the area under the curve (AUC).
The area under the ROC curve (AUC)[1][2] is often used to summarize in a single number the diagnostic ability of the classifier. The AUC is simply defined as the area of the ROC space that lies below the ROC curve.
However, in the ROC space there are regions where the values of FPR or TPR are unacceptable or not viable in practice. For instance, the region where FPR is greater than 0.8 involves that more than 80% of negative subjects are incorrectly classified as positives: this is unacceptable in many real cases. As a consequence, the AUC computed in the entire ROC space (i.e., with both FPR and TPR ranging from 0 to 1) can provide misleading indications.
To overcome this limitation of AUC, it was proposed[3] to compute the area under the ROC curve in the area of the ROC space that corresponds to interesting (i.e., practically viable or acceptable) values of FPR and TPR.
^Van der Schouw, Y.T.; Verbeek, A.; Ruijs, J.H. (1992). "ROC Curves For the Initial Assessment of New Diagnostic Tests". Family Practice. 9 (4): 506–511. doi:10.1093/fampra/9.4.506. ISSN 0263-2136. PMID 1490547.
^Bradley, Andrew P. (1997). "The use of the area under the ROC curve in the evaluation of machine learning algorithms". Pattern Recognition. 30 (7): 1145–1159. Bibcode:1997PatRe..30.1145B. doi:10.1016/S0031-3203(96)00142-2. ISSN 0031-3203. S2CID 13806304.
^Cite error: The named reference :3 was invoked but never defined (see the help page).
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