What is the ROC curve and AUC?

The ROC (Receiver Operating Characteristic) curve plots the True Positive Rate (Recall) against the False Positive Rate at various classification thresholds. It visualizes the tradeoff between sensitivity and specificity. The AUC (Area Under the Curve) summarizes the ROC curve in a single number: AUC = 1.0 means a perfect classifier; AUC = 0.5 means a random classifier. AUC is threshold-independent and works well for imbalanced classes. A good model typically achieves AUC > 0.8.