Visualizing PerformanceConsiderations
In modern machine learning, ROC and AUC are legendary tools for evaluating the performance of a classifier. However, as with everything in life, they aren't completely perfect.
When you absolutely need to know the true, raw likelihood of an event occurring—and not just whether model A is better at ranking threats than model B—ROC and AUC will actually give you no useful information. Furthermore, the AUC metric treats all classification errors as if they are
As we learned in our AI doctor example, missing a cancer diagnosis (a false negative) is practically far more dangerous than causing a false alarm!
Tying It All Together
Even with their flaws, plotting an ROC Curve and calculating its AUC is almost always going to be worth your time. They provide an unbeatable visual breakdown of the hidden strengths, weaknesses, and extreme trade-offs inside your AI.
Next time you build a classifier, don't just blindly rely on basic accuracy. Plot the curve!