### IR F1 Score The F1 Score is the harmonic mean of precision and recall. It is a way of combining both precision and recall into a single measure that captures both properties. An F1 Score is particularly useful when the balance between precision and recall is important. It is a better measure than accuracy, especially in scenarios where the distribution of classes is imbalanced (i.e., non-target content vastly outnumbers target content). **Formula:** F1 Score=2×Precision×RecallPrecision+Recall\text{F1 Score} = 2 \times \frac{\text{Precision} \times \text{Recall}}{\text{Precision} + \text{Recall}}F1 Score=2×Precision+RecallPrecision×Recall​