In this edition, we will explore model evaluation metrics. By the time you’re through, you’ll know how to make sense of precision, recall, F1-score, AUC, and MCC in plain language, and more importantly, when to reach for each one depending on the problem in front of you. I’ll also show you how to implement these metrics in Python using scikit-learn, and how to bring them to life with visualizations like precision-recall and ROC curves.