[scikit-learn] No convergence warning in logistic regression

Sebastian Raschka mail at sebastianraschka.com
Fri Aug 30 12:29:34 EDT 2019

Hi Ben,

I can recall seeing convergence warnings for scikit-learn's logistic regression model on datasets in the past as well. Which solver did you use for LogisticRegression in sklearn? If you haven't done so, have used the lbfgs solver? I.e.,

LogisticRegression(..., solver='lbfgs')?


> On Aug 30, 2019, at 9:52 AM, Benoît Presles <benoit.presles at u-bourgogne.fr> wrote:
> 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
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn

More information about the scikit-learn mailing list