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Add weighted voting to Ensemble Vote meta-learner

Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,408   Unicorn
edited December 2018 in Product Ideas

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.

Brian T.
Lindon Ventures 
Data Science Consulting from Certified RapidMiner Experts
JEdwardyzanThomas_Ottlaith_s_khalafTelcontar120
5
5 votes

Open for Voting · Last Updated

IC-1095

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