What is the F1 score?

• A. Arithmetic mean of precision and recall
• B. Product of precision and recall
• C. Max of precision and recall
• D. Harmonic mean of precision and recall

Answer: (D)

F1 score measures a balance between precision and recall. It is the harmonic mean of those two metrics, calculated as 2 × (precision × recall) divided by (precision + recall). The harmonic mean punishes situations where one value is much lower than the other, so the F1 score stays high only when both precision and recall are relatively strong. This makes F1 a good single-number summary when you care about both false positives and false negatives, especially in datasets with class imbalance. If either precision or recall is poor, the F1 score drops toward zero; if both are high, it approaches one.

In contrast, an arithmetic average would overlook the trade-off between the two, a product could be unduly affected by a single small value, and taking the maximum would ignore the need to maintain both metrics simultaneously.