0
$\begingroup$

I'm evaluating the variability in performance (AUC) in the test set of a machine learning model with an intrinsic random component (xgboost). How many sources of variation should I use?

  • Just replicate the analysis harnessing the model intrinsic randomness?
  • Bootstrap the train set?
  • Bootstrap the test set?
  • Bootstrap both train and test?

Thanks

$\endgroup$

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service, privacy policy and cookie policy

Browse other questions tagged or ask your own question.