Use a relatable analogy (like a spam filter or medical test) to ground the concepts, then define precision and recall in plain terms, and finally connect them to business trade-offs and stakeholder goals. Emphasize that the right balance depends on the cost of false positives versus false negatives.
Pro tip: Ask the stakeholder which error is more costly—missing a positive case or raising a false alarm—and tailor your explanation to that priority; this shows you think in terms of business impact, not just metrics.
Choose a familiar scenario (e.g., email spam filtering or medical testing) to make the concepts concrete. Briefly describe what a 'positive' prediction means in that scenario.
Explain precision as: 'When we say yes, how often are we right?' Use the analogy to illustrate—e.g., of all emails flagged as spam, how many actually are spam.
Explain recall as: 'Of all the actual yes cases, how many did we catch?' Use the analogy—e.g., of all true spam emails, how many did we filter out.
Explain that improving one often reduces the other, and the optimal balance depends on the business cost of false positives vs. false negatives. Give an example of when you'd prioritize each.
Tie the metrics back to the stakeholder's objectives—e.g., for a fraud detection system, high recall might be critical to catch all fraud, while for a recommendation system, high precision might matter more to avoid irrelevant suggestions.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.