title: Does sklearn provide a VotingClassifier for already fit estimators?
body: <p>I have n binary classifiers, and I'd like to see what results I get by combining their predictions, just like a <code>VotingClassifier</code> would do, using majority voting or the sum of the individual predict_proba to determine the final prediction.</p>
  <p>(Looking on google I already found <a href="https://gist.github.com/tomquisel/a421235422fdf6b51ec2ccc5e3dee1b4" rel="nofollow noreferrer">here</a> some guy's implementation of what I need and the code seems correct, I'm just looking for the &quot;sklearn way&quot; of achieving this)</p>
  <p>My problem is that <code>sklearn.ensemble.VotingClassifier</code> is a Soft Voting/Majority Rule classifier for <strong>unfitted</strong> estimators.</p>
  <p><strong>Does sklearn provide something for <strong>fitted</strong> estimators?</strong></p>
  <p>By looking at <code>sklearn.ensemble</code>'s <a href="https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble" rel="nofollow noreferrer">documentation</a> it would seem it does not, but isn't weird that such a functionality would be missing from such a complete library? Am I missing something? Can I get that functionality using features from sklearn or do I have to implement it by myself?</p>