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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](https://us.v-cdn.net/6030995/uploads/userpics/138/nJ7TXYT3J8B7R.png)
![](https://s3.amazonaws.com/rapidminer.community/vanilla-rank-images/unicorn-16x16.png )
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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