Applying post probability adjustments to models that use SMOTE


I am reading the webpage on tidymodels where its mentioned that the probabilities of a trained model
using up/down/SMOTE sampling will not be representative of the actual probabilities in the dataset. I have seen there are some adjustments that can be made post modelling such as Platt Scaling. I have a number of questions, I was hoping you can give me help on

  • Is the adjustment on the probabilities done after you apply your model to the test set and to any new and unseen data going forward. Im thinking about putting a model into production.
  • Is this something that can be done within the tidymodels framework

Thank you for your time

If you did something like that, it would basically undo the subsampling. That's all subsampling methods (like SMOTE) do: make the class probability estimates less consistent with the class prevalence in the actual data.

Postprocessing is on our roadmap and probably won't start until later this year.

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