I am dealing with a tricky, unbalanced data set and trying to run a logistic regression model. One class is present with a 10:1 ratio.
My objective here is to boost my predictive accuracy - minimize the incorrect predictions and maximize the correct ones.
I've tried undersampling (which doesn't work very well) and I have tried logistic regression,case-weighted logistic, and Firth logistic regression.
None of them are very successful.
While I can sometimes yield good true negative rates depending on the dataset, in the end the prediction is merely representative of its underlying class distribution. The case-weighted logistic does about as well as the Firth when I test it - which is to say, it does horribly.
Is there anything else I can do to meet my objective? From what I can tell, exact logistic regression is an option but only for very small datasets, which is not the case here.
Do I need to go back and explore variable selection?
Why is it that the penalized model (Firth) is not a substantial improvement over the case-weighted logistic?
Are there any options I am not considering?
Should I change tactics and look to something like an anomaly detection model instead?
-- Please excuse me if this question is lacking in details -this is my first time asking a question here. I would be happy to add in anything.