ROC Analysis

Receiver operating characteristic (ROC) Curve Analysis is a useful way to assess the accuracy of model predictions. It assesses by plotting sensitivity versus (1-specificity) of a classification test (as the threshold varies over an entire range of diagnostic test results). The full area under an ROC curve, or AUC, formulates an important statistic that represents the probability that the prediction is in the correct order when a test variable is observed (for one subject that is randomly selected from the case group, and the other randomly selected from the control group). ROC Analysis supports the inference regarding a single AUC, precision-recall (PR) curves, and provides options for comparing two ROC curves that are generated from either independent groups or paired subjects.

To know more, go to Base Edition> Core Features>ROC Analysis

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