Having correlated data is not the same as not converging.
We could warn on correlated data but I don't think that's actually
useful for scikit-learn.
I actually recently argued to remove the warning in linear discriminant
analysis:
https://github.com/scikit-learn/scikit-learn/issues/14361
As argued in many places, we're not a stats library and as long as
there's a well-defined solution,
there's no reason to warn.
LogisticRegression will give you the solution with minimum coefficient
norm if there's multiple solutions.
On 9/2/19 5:40 AM, Guillaume Lemaître wrote:
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.pres...@u-bourgogne.fr <mailto:benoit.pres...@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.pres...@u-bourgogne.fr
<mailto: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
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Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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