Cross-Validation returns different model

mark42mark42 MemberPosts:2Contributor I
edited November 2018 inHelp

Hello,

I am using RapidMiner Studio 7.2.002 and recognized something strange after training libSVM (nu-SVC, linear kernel) classifier within X-Validation and outside X-Validation (with the same parameters): the output models are different! According to the documentation of the X-Validation operator the output model is trained on the whole example set, which would be the same as just using the libSVM training.

Do I miss anything or why are these models different although the operators, parameter and data are the same?

Best
Mark

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Answers

  • MartinLiebigMartinLiebig Administrator, Moderator, Employee, RapidMiner Certified Analyst, RapidMiner Certified Expert, University ProfessorPosts:3,404RM Data Scientist

    Hi Mark,

    the only way i can imagine that this one happens is that their is a randomness in the algorithm or the starting points of the optimization make a difference. I've tested it on sonar and saw no difference - but that might not be that meaningful.

    Are you doing some kind of preprocessing inside x-val which might be different?

    ~martin

    - Sr. Director Data Solutions, Altair RapidMiner -
    Dortmund, Germany
  • mark42mark42 MemberPosts:2Contributor I

    Hi Martin,

    I don't use any preprocessing inside the X-Validation: only libSVM, Apply Model and Performance Classification (Accuracy). The data and parameters are the same.

    Best

    Mark

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