Understanding how classification models convert probabilities into decisions is critical in machine learning. This post breaks down the ROC Curve, confusion matrix, and the art of threshold selection. With intuitive examples like spam detection and student placement, you’ll learn how to evaluate classifiers, minimize errors, and choose the best threshold using ROC and AUC-ROC.