The Receiver Operating Characteristic (ROC) curve is a graphical tool for evaluating the performance of a binary classification model.

It plots the True Positive Rate (Sensitivity) against the False Positive Rate (1 - Specificity) at different classification thresholds. A well-performing model has a curve that bends towards the top-left corner, indicating high sensitivity with low false positives.

The Area Under the Curve (AUC) quantifies overall model performance. AUC values range from 0 to 1:

• AUC = 1: Perfect classifier.
• AUC > 0.8: Strong model.
• AUC = 0.5: No predictive power (random guessing).

A higher AUC means better discrimination between classes.

Example of output from a logistic regression model showing AUC.

Next reading:

Precision and Recall