Hello Sebastian,
I have tried with the lbfgs solver and it does not change anything. I do
not have any convergence warning.
Thanks for your help,
Ben
Le 30/08/2019 à 18:29, Sebastian Raschka a écrit :
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')?
Best,
Sebastian
On Aug 30, 2019, at 9:52 AM, Benoît Presles <benoit.pres...@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@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
