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?



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