This article provides an introduction to ROC analysis and serves as a guide for using ROC graphs in research. ROC graphs are useful for visualizing and comparing classifiers based on their performance. They are commonly used in medical decision making and have become increasingly popular in machine learning and data mining research. Although ROC graphs may seem simple, there are common misconceptions and pitfalls when using them in practice. The article explains the concepts behind ROC graphs, including the true positive rate, false positive rate, and other related metrics. It also discusses the properties of ROC graphs, such as their ability to handle skewed class distributions and unequal classification error costs. The article highlights the importance of understanding the characteristics and limitations of ROC graphs to use them effectively in evaluation practices. It also covers the generation of ROC curves, the calculation of the area under the ROC curve (AUC), and the averaging of ROC curves. The article concludes by emphasizing the usefulness of ROC graphs as a tool for visualizing and evaluating classifiers, and the importance of using them wisely to promote better evaluation practices in the pattern recognition community.This article provides an introduction to ROC analysis and serves as a guide for using ROC graphs in research. ROC graphs are useful for visualizing and comparing classifiers based on their performance. They are commonly used in medical decision making and have become increasingly popular in machine learning and data mining research. Although ROC graphs may seem simple, there are common misconceptions and pitfalls when using them in practice. The article explains the concepts behind ROC graphs, including the true positive rate, false positive rate, and other related metrics. It also discusses the properties of ROC graphs, such as their ability to handle skewed class distributions and unequal classification error costs. The article highlights the importance of understanding the characteristics and limitations of ROC graphs to use them effectively in evaluation practices. It also covers the generation of ROC curves, the calculation of the area under the ROC curve (AUC), and the averaging of ROC curves. The article concludes by emphasizing the usefulness of ROC graphs as a tool for visualizing and evaluating classifiers, and the importance of using them wisely to promote better evaluation practices in the pattern recognition community.