I have a large set of feature vectors which I will use to attack a binary classification problem (using scikit learn in Python). Before I start to think about imputation, I am interested in trying to determine from the remaining parts of the data if the missing data are 'missing at random' or missing not at random.
What is a sensible way to approach this question?
It turns out a better question is to ask if the data is 'missing completely at random' or not. What is a sensible way to do that?