I have trained a logistic regression model with an extreme imbalanced classes. I used all techniques (SMOTE, class weight adjustment, decision threshold) to improve performance metrics (Precision and Recall) but results are not very promising - very high false positive class rate :(
I have heard that retraining false positive/negatives could improve the result but I am not sure how the process is. for example should I take false positives, relabel it 50/50 as (1, 0) and train it with the same model? I do highly appreciate if someone can elaborate on process with details:
