"Decision Tree Optimization and Accuracy"

lex_
lex_ New Altair Community Member
edited November 2024 in Community Q&A

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

  • Telcontar120
    Telcontar120 New Altair Community Member
    Answer ✓

    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.  

     

     

Answers

  • MartinLiebig
    MartinLiebig
    Altair Employee

    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

  • Telcontar120
    Telcontar120 New Altair Community Member
    Answer ✓

    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.  

     

     

  • lex_
    lex_ New Altair Community Member

    Thanks for pointing me to the right direction.

     

    The random seed was the issue, resulting in the data sets being different after the split operator.

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