Some of the advantages of using the F measure (weighted harmonic mean) over using the Precision & Recall when evaluating an IR system performance are as follows-
Precision quantifies the number of positive class predictions that actually belong to the positive class.
Recall quantifies the number of positive class predictions made out of all positive examples in the dataset.
F-Measure provides a single score that balances both the concerns of precision and recall in one number.
A measure that combines precision and recall is the harmonic mean of precision and recall, the traditional F-measure or balanced F-score:
