This question already has an answer here:
In the R package ModelMetrics, the auc score as shown in the documentation takes only two inputs;
aucScore <- auc(actual=actuallabels, predicted=predictedlabels)
where the inputs are pretty self explanatory. However, how is the AUC score even calculated here? From my understanding, do we not need the class "probabilities" (scores) produced from the model in order to graph the ROC curve in the first place? Each point on the ROC curve is the TPR vs. the FPR, at varying thresholds of class "probabilities" to trigger a "positive" prediction, if my understanding is correct. So how can we chart the ROC curve and find the area under said curve if we don't have the class probabilities to derive each point on the ROC curve itself? Based off labels alone, I would find that it would be impossible to find this unless there's some underlying estimation going on.
I've also noticed that many other packages also compute the area under the ROC curve by taking only two vectors of labels with seemingly no class probabilities.