We already learned that measuring accuracy is not enough to truly evaluate a classifier. Instead, we relied on precision-recall (P/R) curves to get a deeper understanding of how our classifiers perform.
There is a sister of P/R curves, called receiver-operator-characteristics (ROC), which measures similar aspects of the classifier's performance, but provides another view of the classification performance. The key difference is that P/R curves are more suitable for tasks where the positive class is much more interesting than the negative one or where the number of positive examples is much less than the number of negative ones. Information retrieval ...
