Which metric is the harmonic mean of precision and recall?

• A. Accuracy
• B. Specificity
• C. AUC
• D. F1 score

Answer: (D)

The metric you’re after is the F1 score, which is defined as the harmonic mean of precision and recall. Precision is TP divided by (TP plus FP), while recall is TP divided by (TP plus FN). The F1 score combines them as F1 = 2 × (precision × recall) / (precision + recall). Using the harmonic mean means the score drops significantly if either precision or recall is low, so it rewards a true balance between the two.

This makes F1 especially useful when you care about both false positives and false negatives, such as in imbalanced datasets. Other metrics measure different things: accuracy looks at overall correctness, specificity focuses on correctly identifying negatives, and AUC assesses ranking quality across thresholds. None of these are the harmonic mean of precision and recall, which is why the F1 score is the correct choice.