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A ROC space is defined by FPR and TPR as x and y axes, respectively, which depicts relative trade-offs between true positive (benefits) and false positive (costs). Since TPR is equivalent to sensitivity and FPR is equal to 1 − specificity , the ROC graph is sometimes called the sensitivity vs (1 − specificity) plot.
ROC curves plot the sensitivity of a biomarker on the y axis, against the false discovery rate (1- specificity) on the x axis. An image of different ROC curves is shown in Figure 1. ROC curves provide a simple visual method for one to determine the boundary limit (or the separation threshold) of a biomarker or a combination of biomarkers for ...
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.
The receiver operating characteristic (ROC) ... is the distance between threshold's point and the blue dashed maximum line that bounds the left side of the TOC space.
In the section, "ROC Space", the info-box is referenced as containing evaluation metrics. Perhaps inadvertently, the word metric is hyperlinked to the wiki page on metrics, as in metric space distances. The reader may be inclined to believe from this that the info-box contains metrics. This is not the case.
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Roc Nation’s Co-presidents Shari Bryant and Omar Grant give an exclusive look into their caffeine-free 24/7 daily routines: "You got to be hustling as hard as us."