On the consistency of supervised learning with missing values

arXiv:1902.06931v4 Announce Type: replace-cross
Abstract: In many application settings, the data have missing entries which make analysis challenging. An abundant literature addresses missing values in an inferential framework: estimating parameters and their variance from incomplete tables. Here, we consider supervised-learning settings: predicting a target when missing values appear in both training and testing data. We show the consistency of two approaches in prediction. A striking result is that the widely-used method of imputing with a constant, such as the mean …

abstract analysis application arxiv cs.lg data framework literature math.st missing values parameters stat.ml stat.th supervised learning tables training type values variance