Receiver Operating Characteristic (ROC) analysis is a valuable method for evaluating model predictions by graphing sensitivity (1-specificity) of a classification test as the threshold moves throughout a wide range of diagnostic test outcomes. The whole area under a particular ROC curve, or Area Under the Curve (AUC), represents the likelihood that the prediction will be in the proper order whenever a test parameter is observed for one subject randomly picked from the case group while the other is chosen randomly from the control group. ROC Analysis allows for the inference of a single AUC, precision-recall (PR) curves, and the comparison of two ROC curves obtained from either separate groups or paired subjects.
The true positive rate is plotted even against the False Positive Rate (FPR) to create a ROC curve. The true positive rate is the proportion of positive observations that were properly expected to be positive out of all of them. The false positive rate, on the other hand, is the fraction of negative data that is wrongly projected as positive.
The ROC curve analysis depicts the trade-off between sensitivity and specificity (or TPR) (1 – FPR). Classifiers that produce curves nearer to the top-left quadrant perform better. A random classifier is supposed to deliver points along the diagonal as a baseline (FPR = TPR). The closer the curve gets to the ROC space’s 45-degree, the worse the accuracy of the test.
It should be noted that the ROC is not affected by the class distribution. This makes it valuable for testing classifiers that anticipate infrequent occurrences like illnesses or natural catastrophes. In contrast, performance is assessed by accuracy (TP + TN)/(TP + TN + FN + FP) would favor uncommon event predictors that always predict a negative result.
To compare multiple classifiers, it might be advantageous to summarize each classifier’s performance into a single metric. One popular method is to compute for the Receiver Operating Characteristic Area Under the Curve (AUC).
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Tiara Williamson 