ROC curves are commonly used to characterize the sensitivity/specificity tradeoffs for a binary classifier. Most machine learning classifiers produce real-valued scores that correspond with the strength of the prediction that a given case is positive. Turning these real-valued scores into yes or no predictions requires setting a threshold; cases with scores above the threshold are classified as positive, and cases with scores below the threshold are predicted to be negative. Different threshold values give different levels of sensitivity and specificity. A high threshold is more conservative about labelling a case as positive; this makes it less likely to produce false positive results but more likely to miss cases that are in fact positive (lower rate of true positives). A low threshold produces positive labels more liberally, so it is less specific (more false positives) but also more sensitive (more true positives). The ROC curve plots true positive rate against false positive rate, giving a picture of the whole spectrum of such tradeoffs.
ROC curves are one of the primary techniques for figuring out if a binary classifier “works.”