I am a bit puzzled about the process of experimenting with a model and oversampling and then translating it to the final version of the model that will be used:
I oversample the data (only the training dataset)
Tune parameters and everything else
What do i do next to do it properly?
- Do take all the data and oversample it and then re-train the model
- Do i just take all the data i have and retrain the model without oversampling?
- Or do i just take the model that is trained on the oversampled training data as a 'final model'?
What is the correct way to do things? Thanks!