What is precision and recall in model evaluation?
Updated May 17, 2026
Short answer
Precision measures correctness of positive predictions; recall measures coverage of actual positives.
Deep explanation
Precision = TP/(TP+FP), Recall = TP/(TP+FN). They balance false positives and false negatives.
Real-world example
In medical tests, recall ensures detecting all diseases.
Common mistakes
- Optimizing only one metric.
Follow-up questions
- What is tradeoff?
- When use recall?