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I have a binary dataset which is 99% in one class and 1% in the other class. I MUST create a logistic regression. I have read literature that says both using this dataset as is, or over/undersampling will do a better job.
Given this conflicting information, does anyone have any experience in this area? Would I be better off first balancing the classes (maybe not to the extent of 50:50), or would it not matter for logistic regression.
There are around 15000 data points - 200 of which belong to the minority class.