I'm making a predictive model. I'm thinking of using MI but not sure which imputation method to use. Is there some metrics or graphs one can compute on the data to see which method is best for which predictor? or maybe using many different methods and compare them? if so what should I watch out for when comparing them?
Lastly, how does MI work with k-fold cross validation? Say I want to do 50 of 10 fold cross validation with a data that is multiple imputed with m=5, I would do the imputation first and then do the cross validation on each of the 5 sets of data, and then pool them using mean?