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@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.StackingC...>. 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 <~~~~~~~~~~~~~~~~~~~~~~~~~~~>