I am building a model to predict if a customer will use a coupon or not for a given campaign. I am using logistic regression for this model. I took 5 previous campaigns and generally for each campaign conversion rate is around 10%. Thus, to handle this imbalanced data set and to capture more info I took a stratified sample from this data(whole 5 campaigns) such that there are 50% of sample with negatives and 50% positives. Thus, I am oversampling my positives.
My doubt is if I use logistic regression where it estimates coefficients using maximum log likelihood. Will this oversampling will generate bias results?
Also, I think this oversampling won't create any problem with random forest?