F1 Evaluation metric is more important in any case. F1 score sort maintains a balance between the precision and recall for the classifier. If the precision is low, the F1 is low and if the recall is low again F1 score is low
The F1 score is a number between 0 and 1 and is the harmonic mean of precision and recall
F1 Score = 2 x \(\cfrac{ Precision\, \times \,Recall}{Precision\,+\,Recall}\)
When we have a value of 1 (that is 100%) for both Precision and Recall. The F1 score would also be an ideal 1 (100%). It is known as the perfect value for F1 Score. As the values of both Precision and Recall ranges from 0 to 1, the F1 score also ranges from 0 to 1.
