Which classifiers handle missing values in scikit learn 0.16.1
RandomForests in scikit-learn don't handle missing values at the moment [as of 0.16 and upcoming 0.17], and you do need to impute the values before.
I have data with missing values and I would like to build a classifier for it. I know that scikit-learn will help you impute values for the missing data. However, in my case it is not clear this is the right thing to do or even easy. The problem is that the features in the data are correlated so it's not obvious now to do this imputation in a sensible way.
I  know that in R some of the classifiers (decision trees, random forests) can directly handle missing values without your having to do any imputation.
Can any of the classifiers in scikit learn 0.16.1 do likewise and if so, how should I represent the missing values to help it?  
I have read discussions on the scikit learn github about this topic but I can''t work out what has actually been implemented and what hasn't.