"Decision Tree Optimization and Accuracy"

lex_lex_ MemberPosts:2Contributor I
edited June 2019 inHelp

Hi,

I'm trying to use a decision tree which is nested inside the Optimize Parameters (Grid), focusing on Max. Depth and Min. Gain.

Reconstructing the decision tree using the results obtained above, but without the Optimize Parameters (Grid), the accuracy is lower now.

Why is that so?

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Best Answer

  • Telcontar120Telcontar120 Moderator, RapidMiner Certified Analyst, RapidMiner Certified Expert, MemberPosts:1,635Unicorn
    Solution Accepted

    If you are using cross-validation without a local random seed set then every time you close and re-open that process in RapidMiner you can get a different result even if you don't make any other changes. So I suspect that could be the issue.

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

Answers

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

    Hi,

    what performances are you comparing? The X-Val performances from within the optimization with an X-Val result from without?


    Are the performances comparable w.r.t their std_devs?


    ~Martin

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

    Thanks for pointing me to the right direction.

    随机种子是问题,resulting in the data sets being different after the split operator.

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