I have a few different feature sets (so, with same number of rows and the labels are the same) that I use for different ML models. In my case these sets are DataFrames.
I want to use them to train a StackingClassifier. But the fitting step only allows to specify a single feature set. Goal is to fit clf1 with df1 and clf2 with df2, etc.
stack_clf = StackingClassifier(estimators=[('df1-clf', clf1), ('df2-clf', clf2)],final_estimator=LogisticRegression()) stack_clf.fit(...)
How should I proceed with this kind of situation? I have been trying to find a solution using pipelines but am not sure if that is the way to go. There was a similar question several years ago with voting classifiers, but at the time the solution was apparently not natively supported by SKlearn, and the custom functions suggested were specific to voting classfiers. Thanks in advance for any help.