[scikit-learn] RFE with logistic regression

Benoît Presles benoit.presles at u-bourgogne.fr
Tue Jul 24 17:33:30 EDT 2018


So you think that I cannot get reproducible and consistent results with 
this method ?
If you would avoid RFE, which method do you suggest to find the best 
features ?

Ben


Le 24/07/2018 à 21:34, Gael Varoquaux a écrit :
> On Tue, Jul 24, 2018 at 08:43:27PM +0200, Benoît Presles wrote:
>> 3. With C=1, it seems that I have the same results at each run for all
>> solvers (liblinear, sag and saga), however the ranking is not the same
>> between the solvers.
> Your problem is probably ill-conditioned, hence the specific weights on
> the features are not stable. There isn't a good answer to ordering
> features, they are degenerate.
>
> In general, I would avoid RFE, it is a hack, and can easily lead to these
> problems.
>
> Gaël
>
>> Thanks for your help,
>> Ben
>
>> PS1: I checked and n_iter_ seems to be always lower than max_iter.
>> PS2: my data is scaled, I am using "StandardScaler".
>
>
>> Le 24/07/2018 à 20:33, Andreas Mueller a écrit :
>
>>> On 07/24/2018 02:07 PM, Benoît Presles wrote:
>>>> I did the same tests as before adding fit_intercept=False and:
>>>> 1. I have got the same problem as before, i.e. when I execute the
>>>> RFE multiple times I don't get the same ranking each time.
>>>> 2. When I change the solver to 'sag'
>>>> (classifier_RFE=LogisticRegression(C=1e9, verbose=1, max_iter=10000,
>>>> fit_intercept=False, solver='sag')), it seems that I get the same
>>>> ranking at each run. This is not the case with the 'saga' solver.
>>>> The ranking is not the same between the solvers.
>>>> 3. With C=1, it seems that I have the same results at each run for
>>>> all solvers (liblinear, sag and saga), however the ranking is not
>>>> the same between the solvers.
>
>>>> How can I get reproducible and consistent results?
>>> Did you scale your data? If not, saga and sag will basically fail.
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