Evaluation of Classification Models

In this module, we’ll focus on evaluating classification models and understanding their impact in real-world applications. We’ll begin by discussing why accuracy alone isn’t a reliable metric and how different types of errors affect business decisions. Through the confusion matrix and key performance metrics like precision, recall, and F1 score, we’ll learn to interpret model results with clarity. We’ll also explore advanced evaluation tools like ROC curves and AUC to help us assess model performance across thresholds. By the end of this module, you’ll be able to choose appropriate metrics, interpret model results effectively, and make evaluation decisions that align with real-world goals.