Dear scikit-learn users,
I am using the recursive feature elimination (RFE) tool from sklearn to
rank my features:
from sklearn.linear_model import LogisticRegression
classifier_RFE = LogisticRegression(C=1e9, verbose=1, max_iter=10000)
from sklearn.feature_selection import RFE
rfe = RFE(estimator=classifier_RFE, n_features_to_select=1, step=1)
rfe.fit(X, y)
ranking = rfe.ranking_
print(ranking)
1. The first problem I have is when I execute the above code multiple
times, I don't get the same results.
2. When I change the solver to 'sag' or 'saga' (classifier_RFE =
LogisticRegression(C=1e9, verbose=1, max_iter=10000), 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 I have the same results at each run for the
solver='liblinear', but not for the solvers 'sag' and 'saga'. I still
don't get the same results between the different solvers.
Thanks for your help,
Best regards,
Ben
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