I am trying to do the following:
vc = VotingClassifier(estimators=[('gbc', GradientBoostingClassifier()), ('rf', RandomForestClassifier()), ('svc', SVC(probability=True))], voting='soft', n_jobs=-1, weights=[2, 3, 1]) cross_val_score(vc, X_new, y, n_jobs=-1)
In this, I want to tune the parameter
weights. If I use
GridSearchCV, it is taking a lot of time. Since it needs to fit the model for each iteration. Which is not required, I guess. Better would be use something like
prefit used in
SelectModelFrom function from
Is there any other option or I am misinterpreting something?