Great - thanks! Yes, it would be very nice to have feature names automatically propagate throughout sklearn.
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 <~~~~~~~~~~~~~~~~~~~~~~~~~~~> On Tue, May 5, 2020 at 1:42 PM Guillaume Lemaître <g.lemaitr...@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 <ahow...@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 <ahow...@gmail.com> >> Date: Thu, Apr 30, 2020 at 6:05 PM >> Subject: StackingClassifier >> To: Scikit-learn user and developer mailing list <scikit-learn@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 >> <~~~~~~~~~~~~~~~~~~~~~~~~~~~> >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn >> > > > -- > Guillaume Lemaitre > Scikit-learn @ Inria Foundation > https://glemaitre.github.io/ > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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