you can get the pipeline (with optimized hyperparameters) using
grid_search.best_estimator_. Applying the code of Chris on this estimator
will work.
On Thu, 6 May 2021 at 13:45, mitali katoch wrote:
> Hi Chris,
> I forgot to mention that this pipeline I have used within the GridSearchCV.
> I hav
Hi Chris,
I forgot to mention that this pipeline I have used within the GridSearchCV.
I have done what you suggested early but didn't work, it said:
'GridSearchCV' object has no attribute 'named_steps'.
I somehow figured out now
Thanks for your help though.
Best regards,
Mitali Katoch
On Thu, Ma
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
wrote:
> Dear Scikit team,
>
> I am working with FeatureUnion in the pipeline and best param
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',
SelectFromMo
