- #1
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Hello forum,
I am reading this article on quantum machine learning. At one point in the article (page 7) they plot the ROC curve as background rejection vs. signal efficiency. Researching these concepts (since I did not understand them fully), I read that ROC curves should be plotted as TPR (True Positive Rate) vs. FPR (False Positive Rate). Also (I think), TPR can be called signal efficiency; and FPR can be called background rejection.
Why did they plot the ROC in the article I mentioned the way they did, contrary to what I've reasearched, which is always TPR vs. FPR? Any reason at all? Or is it just incorrect?
Thank you in advance!
I am reading this article on quantum machine learning. At one point in the article (page 7) they plot the ROC curve as background rejection vs. signal efficiency. Researching these concepts (since I did not understand them fully), I read that ROC curves should be plotted as TPR (True Positive Rate) vs. FPR (False Positive Rate). Also (I think), TPR can be called signal efficiency; and FPR can be called background rejection.
Why did they plot the ROC in the article I mentioned the way they did, contrary to what I've reasearched, which is always TPR vs. FPR? Any reason at all? Or is it just incorrect?
Thank you in advance!