[scikit-learn] Fwd: StackingClassifier

[Andrew Howe]

Great - thanks! Yes, it would be very nice to have feature names
automatically propagate throughout sklearn.

Andrew

<~~~~~~~~~~~~~~~~~~~~~~~~~~~>
J. Andrew Howe, PhD
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On Tue, May 5, 2020 at 1:42 PM Guillaume Lemaître <g.lemaitre58 at gmail.com>
wrote:

> Your analysis is correct:
> https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/ensemble/_stacking.py#L59
>
> It will be the prediction of each learner in the order in the list given
> and finally the features which are pass-through.
>
> It would nice when we will be able to propagate feature names :)
>
> On Tue, 5 May 2020 at 14:31, Andrew Howe <ahowe42 at gmail.com> wrote:
>
>> Hi All - gentle nudge in case anybody has an idea about this.
>>
>> Andrew
>>
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>> J. Andrew Howe, PhD
>> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
>> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
>> Open Researcher and Contributor ID (ORCID)
>> <http://orcid.org/0000-0002-3553-1990>
>> Github Profile <http://github.com/ahowe42>
>> Personal Website <http://www.andrewhowe.com>
>> I live to learn, so I can learn to live. - me
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>>
>>
>> ---------- Forwarded message ---------
>> From: Andrew Howe <ahowe42 at gmail.com>
>> Date: Thu, Apr 30, 2020 at 6:05 PM
>> Subject: StackingClassifier
>> To: Scikit-learn user and developer mailing list <scikit-learn at python.org
>> >
>>
>>
>> Hi All
>>
>> Quick question about the stacking classifier
>> <https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.StackingClassifier.html>.
>> How do I know the order of the features that the final estimator uses? I've
>> got an example which I've created like this (the LGRG and KSVM objects were
>> previously defined, but as they seem they would be):
>>
>> passThrough = True
>> finalEstim = DecisionTreeClassifier(random_state=42)
>> stkClas = StackingClassifier(estimators=[('Logistic Regression', LGRG),
>> ('Kernel SVM', KSVM)],
>>                              cv=crossValInput, passthrough=passThrough,
>> final_estimator=finalEstim,
>>                              n_jobs=-1)
>>
>> Given this setup, I *think* the features input to the final estimator are
>>
>>    - Logistic regression prediction probabilities for all classes
>>    - Kernel SVM prediction probabilities for all classes
>>    - original features of data passed into the stacking classifier
>>
>> I can find no documentation on this, though, and don't know of any
>> relevant attribute on the final estimator. I need this to help interpret
>> the final estimator tree - and specifically to provide feature labels for
>> plot_tree.
>>
>> Thanks!
>> Andrew
>>
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
>> J. Andrew Howe, PhD
>> LinkedIn Profile <http://www.linkedin.com/in/ahowe42>
>> ResearchGate Profile <http://www.researchgate.net/profile/John_Howe12/>
>> Open Researcher and Contributor ID (ORCID)
>> <http://orcid.org/0000-0002-3553-1990>
>> Github Profile <http://github.com/ahowe42>
>> Personal Website <http://www.andrewhowe.com>
>> I live to learn, so I can learn to live. - me
>> <~~~~~~~~~~~~~~~~~~~~~~~~~~~>
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>
>
> --
> Guillaume Lemaitre
> Scikit-learn @ Inria Foundation
> https://glemaitre.github.io/
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