[scikit-learn] RFE with logistic regression
gael.varoquaux at normalesup.org
Tue Jul 24 15:34:31 EDT 2018
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
> Thanks for your help,
> 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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