ROC-AUC curve as metric for binary classifier without machine learning algorithm 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?
Your intuition is correct: you don't necessarily need a machine learning algorithm to calculate a ROC curve and, from what you say, it seems you already have the necessary ingredients in place.

In fact, the ROC curve was historically developed under circumstances that had nothing to do with machine learning; from Wikipedia:


  The ROC curve was first developed by electrical engineers and radar engineers during World War II for detecting enemy objects in battlefields and was soon introduced to psychology to account for perceptual detection of stimuli. ROC analysis since then has been used in medicine, radiology, biometrics, forecasting of natural hazards, meteorology, model performance assessment, and other areas for many decades and is increasingly used in machine learning and data mining research.