In our data science teaching, we present the ROC plot (and the area under the curve of the plot, or AUC) as a useful tool for evaluating score-based classifier models, as well as for comparing multiple such models. The ROC is informative and useful, but it’s also perhaps overly concise for a beginner. This leads to a lot of questions from the students: what does the ROC tell us about a model? Why is a bigger AUC better? What does it all mean?
Read on for the answer.