“A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The ROC curve is created by plotting the true positive rate against the false positive rate at various threshold settings.” – Wikipedia
Simulation can be very useful for us to understand some concepts in Statistics, as shown in Probability in R. Here is another example that I used simulation to understand ROC Curve and AUC, the metrics in classification models that I had never fully understand.
Data
The simulation in this post was inspired by OpenIntro Statistics and the email dataset I used can be found in openintro package.