I am new to logistic regression and I am trying to determine whether it makes sense to weight a particular case in my data which is oversampled in order to model my data better. I'm not even sure if this is something that can be done, but I was wondering if anyone knew for sure. I know you can apply a greater weight to one of your binary labels (dependent variables) in order to make that class more important. But I'm actually trying to weight particular cases in the data - more from the standpoint of features in the model. So for example if I was modeling whether or not someone is going to pass a kidney stone and my inputs were age and amount of caffeine consumed, and it so happens I have sampled a ridiculously large amount of data from people who are over the age of 60 and drink more than 4 cups of coffee a day, and I think that is going to really inflate that "scenario", could I put a slightly lesser weight on all of these cases and mitigate the problem?
Sorry if this question is confusing or less technical than it should be for this site. I tried googling but had a tough time finding information about the use cases for applying weighting in logistic regression.