Not sure if this is a great place for this question, but I was told CrossValidated was not. So, all these questions refer to sklearn, but if you have insights into logistic regression in general, I'd love to hear them as well.
1) Does data have to be standardizes(mean 0, stdev 1)?
2) In sklearn, how do I specify what kind of regularization I want (L1 vs L2)? Note that this is different from penalty; penalty refers to classification error, not pentalty on coefficients.
3) How can I use to also do variable selection? I.e., analogously to lasso for linear regression.
4) When using regularization, how do I optimize for C, the regularization strength? Is there something built-in, or do I have to take care of this myself?
Probably an example would be most helpful, but I'd appreciate any insights on any of these questions.
This has been my starting point: http://scikit-learn.org/stable/modules/generated/sklearn.linear_model.LogisticRegression.html
Thank you very much in advance!