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Is there a way to tune sample size as a hyperparameter for Random Forest?

SemsemponponSemsemponpon Member Posts: 1 Learner I
in Help
Along these hyperparameters, I also want to tune sample size. I tried using sampling operators in RM but those are not random like what would you get for bootstrapping. I there any way to that or am I missing something?
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Answers

  • BalazsBaranyBalazsBarany Administrator, Moderator, Employee-RapidMiner, RapidMiner Certified Analyst, RapidMiner Certified Expert Posts: 955 Unicorn
    Hi,

    how did you try to use Sample and what didn't work? This would be the way to go if you want to sample the incoming data of the random forest.

    There are many sample sizes involved with random forests. One is controlled by the "subset ratio" setting that you'll see when unchecking "guess subset ratio". 

    I'm not sure if you gain anything by tuning the number of folds of Cross Validation. What would you do with that result? Select the best-performing validation fold size? That's sheer luck and not a good assessment of the model quality. Also, the validation method doesn't change your model in the end.

    Regards,
    Balázs
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