[scikit-learn] No convergence warning in logistic regression

[Guillaume Lemaître]

LBFGS will raise ConvergenceWarning for sure. You can check the n_iter_
attribute to know if you really converged.

On Mon, 2 Sep 2019 at 10:28, Benoît Presles <benoit.presles at u-bourgogne.fr>
wrote:

> 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.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
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-- 
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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