You have two models. Model A has F1 of 0.82. Model B has F1 of 0.79.

Model A wins, right?

Not necessarily. F1 is calculated at one specific threshold. Maybe Model B is much better at other thresholds. Maybe on your actual deployment threshold, B beats A.

ROC curves show you the full picture. They plot model performance across every possible threshold at once. AUC collapses that into one number you can compare.

It's the right way to compare classifiers when you haven't committed to a threshold yet.

What You'll Learn Here

What the ROC curve actually plots and how to read it

What AUC means in plain language

How to build and compare multiple ROC curves

When to use ROC-AUC vs precision-recall

Multi-class ROC with one-vs-rest

The things people get wrong about AUC

The Two Axes of a ROC Curve

ROC stands for Receiver Operating Characteristic. It comes from signal detection theory in the 1940s. The name is not helpful.…