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 a
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
wrote:
> Hello Sebastian,
>
> I have tried with the lbfgs solver and it does not change anything. I do
> not have any convergence warning
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
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,
S
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 t
