ROC Curve

The ROC curve, or Receiver Operating Characteristic curve, is a graphical representation used to evaluate the performance of a binary classification model. It plots the true positive rate (sensitivity) against the false positive rate (1-specificity) at various threshold settings. The curve illustrates the trade-off between sensitivity and specificity, providing insights into the model's ability to distinguish between classes. A model with a curve closer to the top-left corner indicates better performance, while the area under the curve (AUC) quantifies the overall capability of the model. Common use cases include assessing diagnostic tests, machine learning classifiers, and fraud detection systems.