The F1 score is described as which of the following?

• A. Harmonic mean of precision and recall
• B. Arithmetic mean of accuracy and precision
• C. Geometric mean of recall and specificity
• D. Sum of precision and recall

Answer: (A)

The F1 score measures how well a model balances precision and recall by using their harmonic mean. Precision is the proportion of retrieved items that are actually relevant, and recall is the proportion of all relevant items that were retrieved. The F1 score combines these two into a single value with F1 = 2 × (precision × recall) / (precision + recall). The harmonic mean punishes imbalance: if either precision or recall is low, the F1 score drops significantly, even if the other value is high. This makes F1 especially useful when you care about both false positives and false negatives.

So the best description is that the F1 score is the harmonic mean of precision and recall. It’s not based on accuracy or specificity, and it’s not the arithmetic mean or geometric mean of those or other metrics, nor the simple sum of precision and recall.