Precision and recall metrics for product managers

Learn how to explain precision and recall to stakeholders without getting lost in the math. Product managers need to know if an AI model is good, but accuracy is a vanity metric that hides real business impact. This lesson uses a fishing net metaphor to break down evaluation metrics. You will learn how precision measures the cleanliness of your catch, while recall checks if you miss the big targets. We explore the F1 score as a referee that prevents cheating with extreme approaches. We apply these concepts to scenarios like hospital spam filters to show why false alarms and missed targets carry different costs. You will discover how to negotiate the cost of being wrong and instruct engineering to tune the model for specific pains. In this lesson: - Precision as trust and recall as coverage - Using the F1 score to balance model behaviors - Choosing between false alarms and missed targets - Reporting metrics without relying on accuracy U2xAI Academy - AI skills for product managers.