I have a highly imbalanced dataset (0.21 percent positives, rest negatives) for which I am trying to build a classifier.
I tried to improve the F1 scores using hyperparameter tuning but in all the iterations, I got either good recall or good precision scores. Never the both. One came at the cost of another.
Is there a way to use these two models to improve the F1 and reduce the number of false positives being produced by the model with good recall but bad precision.