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Combining Multiple Imputation in Rapid miner
I don`t know what I should do with these imputations of the data. Should I train all my base learners with all these imputations individually?
That sounds right, but it takes a lot of time to train each of the base learners with each of the imputed data sets and then again train the stacked model with each of the imputed data sets!
Anyway, if that`s right, how can I combine the five models learned by 5 imputed data sets?
I mean, for example, to combine models for a stacking model, or addaboost or ... there are operators, but to combine models built from different imputed data sets, I couldn`t find any operator!