I found the ROC explanation at this link. It states that the ROC curve is TP vs FP.
After the score has gone below 0.5, all predictions are negative. That makes them either TN or FN. Thus, how does it make sense to continue drawing as the axes are stated as TP and FP? I am really confused.
Best Answer
The ROC curve shows the TPR and FPR as you change the threshold. Your question posits that a classifier only has one TPR statistic and one FPR statistic, and that both of these statistics correspond to a threshold at 0.5. This is a common misconception; there is no reason that the threshold must be 0.5. For each choice of threshold, there is a corresponding TPR and FPR statistic. The purpose of the ROC curve is to show the trade-off for each choice of threshold: as your TPR increases, so does your FPR, and vice-versa.