Hi, Assuming that you have trained your pipeline, the following piece of code should work.
pipeline.named_steps["feature_sel"].transform(X) Best, Chris On Thu, May 6, 2021 at 12:52 PM mitali katoch <mitalikat...@gmail.com> wrote: > Dear Scikit team, > > I am working with FeatureUnion in the pipeline and best parameters are as > follows: > Pipeline(steps=[('feature_sel', > FeatureUnion(transformer_list= [ ('selectk', > SelectKBest(k=500)), > ('sel_fromModel', > > SelectFromModel(estimator=LogisticRegression(C=1, > > penalty='l1', > > solver='liblinear'), > > max_features=100))] > )), > ('sampler', SMOTE(k_neighbors=2, random_state=10)), > ('model', SVC(random_state=10))] > ) > > I would like to extract those SelectKBest(k=500) and max_features=100 from > the pipeline. > > Could you please confirm whether it is possible to do it, If yes, could > you share the solution, I would highly appreciate that. > > Thanks in advance. > > Best Regards, > Mitali Katoch > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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