Maybe it's a trivial question, but I'm a bit confused right now... I'll explain: I've some elements in my data, each with a value between 0 and 1 and an associated label (1, 0). I need to test some thresholds and then compute the ROC-AUC curve. For example with a threshold = 0.4, all the values greater than 0.4 will be predicted as true (1) and all the values under 0.4 will be predicted as false (0), then I'll compare the result with the actual labels to calculate the True Positive Rate and the False Positive Rate and finally build the ROC-AUC curve.
So, my question is, considering that, based on the threshold chosen, I already know under which prediction an element will fall, I don't need a machine learning classifier to do the default training and testing stages, right?