I'm optimizing a logistic regression in sklearn through repeated kfold cross validation. I want to checkout the confidence interval and, based on other stack exchange answers, it seems easier to get that info from statsmodels.
Coming from a basis in sklearn though, statsmodels is opaque. How can I translate the optimized settings for my logistic regression into statsmodels. Things like the L2 penalty, C value, intercept, etc.?
I've done some research and it looks like statsmodel support L2 indirectly through GLM Binomials. The C value will need to be converted from C into alpha (whatever that means), and I have only a vague idea of how to specify the intercept (it looks like it has something to do with the add_constant function).
Can someone drop an example of how to do this kind of translation into statsmodels? I'm sure once I see it done, a lot of it will naturally fall into place in my head.