I'm going with C. Recalculating the imputations using the validation data alone ensures we're not introducing any bias from the training set. Simple yet effective!
Option D makes the most sense to me. Using both the training and validation data for the imputations will give us a more robust and unbiased model evaluation.
If the imputed values are irrelevant to the validation data, how do we ensure the model's performance is consistent across both datasets? Hmm, tough one.
Delsie
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