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VOTING MATTERS!
IDEAS WITH HIGH NUMBERS OF VOTES (USUALLY ≥ 10) ARE PRIORITIZED IN OUR ROADMAP.
NOTE: IF YOU WISH TO SUGGEST A NEW FEATURE, PLEASE POST A NEW QUESTION AND TAG AS "FEATURE REQUEST". THANK YOU.
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Add weighted voting to Ensemble Vote meta-learner
Telcontar120
Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,635 
Unicorn
The ensemble meta-learner Vote allows you to combine predictions from individual models, but it currently only provides simple majority voting for classification problems. For classification problems, it would be helpful to add a parameter to allow weighted voting (basically to average the confidences of the individual components rather than 0/1 voting by classification). This is similar to what is already supported in individual learners like k-nn for example. With only majority voting, the resulting classification confidences are very "lumpy" which is unfavorable for many reasons.
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