The article provides an introduction to Receiver Operating Characteristic (ROC) graphs, which are widely used in medical decision-making and increasingly in machine learning and data mining. ROC graphs visualize and organize classifier performance, showing the trade-offs between true positive rates and false positive rates. The author discusses the conceptual simplicity of ROC graphs while highlighting common misconceptions and pitfalls in their practical use. The article covers classifier performance metrics, the ROC space, and methods for generating ROC curves efficiently. It also explores the ROC convex hull, which identifies potentially optimal classifiers, and the area under the ROC curve (AUC), a scalar measure of classifier performance. The article further discusses averaging ROC curves, handling multi-class problems, and interpolating classifiers to achieve desired performance levels. The goal is to advance understanding and promote better evaluation practices in pattern recognition and machine learning.The article provides an introduction to Receiver Operating Characteristic (ROC) graphs, which are widely used in medical decision-making and increasingly in machine learning and data mining. ROC graphs visualize and organize classifier performance, showing the trade-offs between true positive rates and false positive rates. The author discusses the conceptual simplicity of ROC graphs while highlighting common misconceptions and pitfalls in their practical use. The article covers classifier performance metrics, the ROC space, and methods for generating ROC curves efficiently. It also explores the ROC convex hull, which identifies potentially optimal classifiers, and the area under the ROC curve (AUC), a scalar measure of classifier performance. The article further discusses averaging ROC curves, handling multi-class problems, and interpolating classifiers to achieve desired performance levels. The goal is to advance understanding and promote better evaluation practices in pattern recognition and machine learning.