(Before someone marks this as duplicate - I'm not asking about training data, I'm asking about new data which has come in and needs to be classified)
Suppose I've got a dataset which has 5 predictors - v1 - v5. Since my dataset contains NA's, I've been imputing these values during training, which is fine.
However, if I get a new item to classify, it may contain 1 or more NA's. My initial thought was to create a model for each predictor variable; so for example, create a model where v2 - v5 predict v1, in the case that v1 is missing.
This would result in 5 additional models. One problem is that if v1 and v2 are missing, I'd need to guess both values somehow. I'm not sure how to do this. It makes me think that this isn't the best approach.
I'm a bit stuck, and any insight into this would be much appreciated :)