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Multinomial Logistic Regression in Rapidminer

sunnyalsunnyal Member Posts: 44 Contributor II
edited December 2018 in Help

Hi,

 

I wanted to perform a Multinomial Logistic Regression for designating our customer types. I do not see any operator for this can you provide some guidance in this regards

 

Thx

 

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Best Answer

  • Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761 Unicorn
    Solution Accepted

    @sunnyal the Generalized Linear Model operator can do Multinominal labels, just have to set the Family parameter to multinominal

Answers

  • rfuentealbarfuentealba RapidMiner Certified Analyst, Member, University Professor Posts: 568 Unicorn

    Hi @sunnyal,

     

    Is there any difference between polynomial and multinomial logistic regressions? Because if there isn't a difference, I remember that there is an operator named Logistic Regression (Evolutionary), that you can configure to do polynomial regressions.

     

    All the best,

     

  • sunnyalsunnyal Member Posts: 44 Contributor II

    Thank you Rodrigo, Tom,

     

    I also see an operator called Polynomial regression. Would this suffice the need for performing multinomial regression?? Is there a difference between this an Logistic Regression (Evolutionary) and Generalized Linear Model ??

     

    Also, do we have an sample process on Generalized Linear Model with family type as multinomial, that I can infer ??

     

    Thx 

  • Thomas_OttThomas_Ott RapidMiner Certified Analyst, RapidMiner Certified Expert, Member Posts: 1,761 Unicorn

    @sunnyal The Polynominal Regression operator can only use a numerical label with numerical labels, so you can't use it for a multi-label application. The Logistic Regression (Evolutionary) operator is a lot like a standard LR algo BUT uses a Support Vector Machine to determine the boundary conditions of a binomal label. So that won't work either if you have multi-labels (i.e. more than 2). 

     

    The best bet, IMHO, is to use the GLM operator. It's a modern implementation and really fast. 

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