Classification problems are at the heart of many machine learning applications, from spam detection to disease diagnosis. However, measuring the performance of classifiers is crucial for selecting the right model and ensuring reliability. In this detailed guide, we will explore the various metrics used to evaluate classification models, including accuracy, precision, recall, F1-score, and AUC-ROC, to help you make informed decisions in your machine learning projects.