# How does Python Scikit Learn handle linear separation problem in logistic regression?

There are already posts about warnings from R dealing with logistic regression and linear separation such as this one. I just wanna make sure if in Python Scikit Learn this problem is all solved by the L1/L2 regularization part in the optimization function. In other words, it's safe to say users will not get any infinite MLE estimates warnings from sklearn.linear_model.LogisticRegression?

Yes, sklearn.linear_model.LogisticRegression uses penalized logistic regression which "solves" the problem of perfect separation. If you set C to something too large, you might still end up with bad results, though.