Dear all,
I compared the logistic regression of statsmodels (Logit) with the
logistic regression of sklearn (LogisticRegression). As I do not do
regularization, I use the fit method with statsmodels and set
penalty='none' in sklearn. Most of the time, I have got the same results
between the two packages.
However, when data are correlated, it is not the case. In fact, I have
got a very useful convergence warning with statsmodel
(ConvergenceWarning: Maximum Likelihood optimization failed to converge)
that I do not have with sklearn? Is it normal that I do not have any
convergence warning with sklearn even if I put verbose=1? I guess
sklearn did not converge either.
Thanks for your help,
Best regards,
Ben
