I did the same tests as before adding random_state=0 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' or 'saga'
(LogisticRegression(C=1e9, verbose=1, max_iter=10000,
fit_intercept=False, random_state=0, solver='sag')), it
seems that I get the same results at each run but the ranking is not the
same between these two 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.
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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