64. Precision and Recall: Beyond Accuracy

Short summary

Precision and recall measure fundamentally different aspects of model performance: precision tracks false alarms, while recall tracks missed detections. They trade off against each other—improving one typically hurts the other—so choose based on which error type is costlier for your problem. Use F1 score for balanced tradeoffs, or tune the decision threshold when false positives and false negatives have different costs.

• •Precision = false alarm rate; recall = miss rate
• •Higher precision means fewer false positives; higher recall means fewer false negatives
• •Choose metric based on cost of errors; use F1 for balance or threshold tuning for asymmetric costs

Generated with AI, which can make mistakes.