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How is data split into training / test sets in rapidminer GO?

cramsdencramsden Member Posts: 32 Contributor I
I am using rapid miner go and would like to know how data is being split into training / test sets and if its the same for each method (deep learning, gradient boosted trees etc.).

Is there anywhere in the docs that say first 70% of rows are used for training or something like this?

Thank you

Answers

  • aleboalebo Employee, Member Posts: 8  RM Product Management
    Hi Chris,Β 
    We use a 60/40 split for every model. If the target column is nominal, Go builds random subsets and ensures that value distribution is the same as in the original dataset. Otherwise, Go builds subsets randomly.Β 
    Regards,
    Andras
  • cramsdencramsden Member Posts: 32 Contributor I
    Thank you,

    Is there anymore information on this?Β  I am new to data science and self teaching, so I'm a bit confused by the terminology.Β Β 

    I am asking because I am noticing a difference in the predictive power of my models based on which order the data set they were built on was originally uploaded.

    To clarify, the data is 60 / 40 split but what goes into the 60 and 40 respectively is done randomly but ensuring the same distribution is kept?

    Or is it the first 60% of rows and last 40% of rows for the split?


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