What do precision and recall mean?
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
Precision says what share of the results the system pointed to were actually relevant. Recall says what share of all the relevant results the system found at all. These are two different problems: showing junk and missing valuable things.
Example: there are 10 matching offers. The system shows 5, of which 4 match. Precision is 4/5, i.e. 80%, and recall is 4/10, i.e. 40%. It looks fairly clean but misses most of the good offers. This matters especially with a cache of rejections. Checking only the accepted records isn't enough to know what was missed.
Why it matters: Helps you understand measuring classification and search.
Possible follow-ups: When does missing a result hurt more, and when does a wrong acceptance?
How did it go? Be honest — “Not yet” puts the question on your review list.
Practise in code
- Does the search find the right material? — runs tests in your browser
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.