I want to train a binary classification net (for NLP) where one class is much more frequent then the other (using Keras).
I have learned that in this case (one class is much more frequent then the other) it is not useful to talk about accuracy. It is more useful to talk about F1 score or to look at ROC curves or talk about the ROC AUC value.
The problem is that you can not directly optimize for those values (loss function can not be given). But what do I have to do in this case? What is the workflow to get a good F1 Score or high ROC AUC value?
Should I use class_weight argument of the fit function in Keras in this case?