The only way for inferior models to overrule the best (expert) model is for them to collectively and confidently agree on an alternative.
To avoid this scenario, we can use a weighted majority vote—but why weighting?
Usually, we want to give a better model more weight in a vote. The simplest, but computationally inefficient, way to do this is to repeat the classifier pipelines under different names, as follows:
weighted_voting_clf = VotingClassifier(estimators=[('lr', lr_clf), ('lr2', lr_clf),('rf', xtc_clf), ('mnb2', mnb_clf),('mnb', mnb_clf)], voting='soft', n_jobs=-1)weighted_voting_clf.fit(X_train, y_train)
Repeat the experiment with hard voting instead of soft voting. This will tell you how the voting strategy ...
