Building a machine learning model is only half the battle. To deploy with confidence, you must understand how to measure success. This session dives deep into the "truth" behind the numbers—moving from basic train-test splits to the nuances of the Confusion Matrix. We will explore why Accuracy is often a trap, how to navigate the Precision-Recall trade-off, and how to interpret ROC Curves to ensure your model performs where it matters most: the real world.