When to use f1_macro vs f1_weighted? My dataset is imbalanced. The labels are binary. I'm training 2 logistic regression models.
For the first one, I didnt balance the model with class_weight and used f1_macro. Was it correct for me to use f1_macro?
for the 2nd Logistic model I set class_weight='balanced'. Should I use f1_macro or f1_wighted here?


There is no single correct answer to this problem. Which metric and loss function to use depends on your problem, data, and business case. Each choice has advantages and disadvantages.
Using class weight in LogisticRegression affects your loss function, and makes the loss function put equal importance to the classes so that the minority class doesn't get lost in the calculations.
The scoring variable in grid search object affects how the test success is measured. The F1 score depends on which class is assumed to be positive, therefore calculating F1 score for both classes and averaging them is a good idea but doing this weighted or not usually depends on your problem. If you want to treat the classes equally, you can do weighted mean. But, if your problem cares more about the positive class, you may do a plain average.
