How do you choose quality metrics and a decision threshold?
Review note: The answer refers to the original author's CV or target job offer — adapt it to your own experience.
Answer out loud or in your head first, then compare.
Answer
First I establish which error hurts more. When searching offers, missing a good result may matter more than showing a few extra ones. When automatically approving a financial document, a false approval can be far more expensive. So I don't choose a metric just because it's popular.
Precision and recall describe different kinds of accuracy. You can also create an uncertainty zone that a person reviews. Calibration means checking whether the stated confidence matches how often results are actually correct. A score of 0.9 or a sum of weights doesn't automatically become 90% confidence. I choose the threshold on examples and check how it works for different groups of data.
Why it matters: Takes the simple example from J-P04 to a product decision.
Possible follow-ups: How often do you need to re-check the threshold after the data changes?
How did it go? Be honest — “Not yet” puts the question on your review list.
Start with
- J-P01 · What is evaluating an AI system, i.e. an eval? — Evaluation, quality and production AI
- J-P02 · How does an eval differ from an ordinary code test? — Evaluation, quality and production AI
Further reading
- Google ML Crash Course: Accuracy, precision, recall(opens in a new tab)Docs; accessed 22.10.2026. The confusion matrix and trade-offs in choosing metrics.