Choosing good classifiers for forward selection applied on nominal data

green_teagreen_tea Member Posts: 8 Learner I
edited January 12 in Help
Hello community,
my goal is to run a wrapper-based feature selection on ~70 nominal features to select a the ~10 best ones. I think a forward selection is the best choice here as it starts with no features and adds one new feature at a time. I read through several guides here on how to do a wrapper-based feature selection that were very helpful in implementing this.
However I am still lost on which classifiers I should select inside the model. I will not use the resulting dataset to train and test a model afterwards, so the obvious choice of selecting the same classifier as I would for the model is not there. Are there any posts here I missed so far that would help me with selecting classifiers? Or can you share your knowledge and experience on this with me? I greatly appreciate your answers!


  • mschmitzmschmitz Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University Professor Posts: 1,872  RM Data Scientist
    i am not sure what the best selection is, but Naive Bayes should be in. At least as a base line.

    - Head of Data Science Services at RapidMiner -
    Dortmund, Germany
  • lionelderkrikorlionelderkrikor Moderator, RapidMiner Certified Analyst, Member Posts: 615   Unicorn
    Hi @green_tea,

    Don't focus only on the "forward selection".
    I think that in life (and thus in data-science), it is always relevant to compare.
    RapidMiner propose several methods of feature selection : 

    You can test them.
    From my own experience, the Optimize Selection (Evolutionary) operator gave always me good results...

    To conclude, here a link to a ressource relativ to Feature Selection :

    Hope it helps,


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