The harmonic mean of precision and recall is used to calculate the F-measure, giving each the same weight. It enables model evaluation by considering both precision and recall using a single score, which is beneficial when explaining the model performance and comparing models.

F1 = 2TP/ 2TP + FP + FN

A beta configuration parameter is added to the Fbeta-measure, a generalisation of the F-measure. The F-measure is equal to 1.0, the same as the default beta value. In the calculation of the score, a smaller beta value, such as 0.5, gives more weight to precision and less to recall, whereas a greater beta value, such as 2.0, gives more weight to recall and less weight to precision.

It is a helpful metric to consider when recall and precision are both crucial, but a little more focus is required on one or the other, such as when false negatives are more significant than false positives or vice versa.