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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