I agree with you about imputation being suboptimal, but there really arent that many classifiers that can natively deal with missing features. Bayesian methods

I agree with you about imputation being suboptimal, but there really arent that many classifiers that can natively deal with missing features. Bayesian methods are the best, where you can marginalize over any missing variables, and a few tree based methods seem to have capacity for handling missing. But Things like logistic regression require imputation. And neural nets may be able to get away with some missing data if you have lots of exmamples of missing data in the training set, but its not really native. A lot of classifier libraries just can't deal with missing stuff, so poor imputation is unfortunately necessary. The thing with bayesian classifiers is that you generally need to understand the problem domain a lot better to get good results out of them than e.g. neural nets.