With a ROC curve, you're trying to find a good model that optimizes the trade off between the False Positive Rate (FPR) and True Positive Rate (TPR). What counts here is how much area is under the curve (Area under the Curve = AuC). The ideal curve in the left image fills in 100%, which means that you're going to be able to distinguish between negative results and positive results 100% of the ti…
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