StackingClassifier: Different Feature Sets 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.

StackingClassifier does not support training each base estimator on different feature sets. I do not see how you could use a Pipeline to cope with this either. With its current implementation, you need to call the fit method with one feature set for all classifiers because StackingClassifier will otherwise not expose attributes ending a trailing _ which are needed to identify the classifier as fitted.
If you are not bound to use scikit-learn, an easy way out would be to use the h2o library and train a H2OStackedEnsembleEstimator. As written in the documentation, it explicitly supports training each base estimator on different feature sets:

The models must be trained on the same training_frame. The rows must be identical, but you can use different sets of predictor columns, x, across models if you choose.

An example for Python can be found here.
