Ben,
I can confirm your results with penalty='none' and C=1e9. In both cases,
you are running a mostly unpenalized logisitic regression. Usually
that's less numerically stable than with a small regularization,
depending on the data collinearity.
Running that same code with
- larger penalty ( smaller C values)
- or larger number of samples
yields for me the same coefficients (up to some tolerance).
You can also see that SAGA convergence is not good by the fact that it
needs 196000 epochs/iterations to converge.
Actually, I have often seen convergence issues with SAG on small
datasets (in unit tests), not fully sure why.
--
Roman
On 09/10/2019 22:10, serafim loukas wrote:
The predictions across solver are exactly the same when I run the code.
I am using 0.21.3 version. What is yours?
In [13]: import sklearn
In [14]: sklearn.__version__
Out[14]: '0.21.3'
Serafeim
On 9 Oct 2019, at 21:44, Benoît Presles <benoit.pres...@u-bourgogne.fr
<mailto:benoit.pres...@u-bourgogne.fr>> wrote:
(y_pred_lbfgs==y_pred_saga).all() == False
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