Missing values in scikits machine learning
When you run into missing values on input features, the first order of business is not how to impute the missing. The most important question is WHY SHOULD you. Unless you have clear and definitive mind what the 'true' reality behind the data is, you may want to curtail urge to impute. This is not about technique or package in the first place. 
Historically we resorted to tree methods like decision trees mainly because some of us at least felt that imputing missing to estimate regression like linear regression, logistic regression, or even NN is distortive enough that we should have methods that do not require imputing missing 'among the columns'. The so-called missing informativeness. Which should be familiar concept to those familiar with, say, Bayesian. 
If you are really modeling on big data, besides talking about it, the chance is you face large number of columns. In common practice of feature extraction like text analytics, you may very well say missing means count=0. That is fine because you know the root cause. The reality, especially when facing structured data sources, is you don't know or simply don't have time to know the root cause. But your engine forces to plug in a value, be it NAN or other place holders that the engine can tolerate, I may very well argue your model is as good as you impute, which does not make sense. 
One intriguing question is : if we leave missingness to be judged by its close context inside the splitting process, first or second degree surrogate, does foresting actually make the contextual judgement a moot because the context per se is random selection? This, however, is a 'better' problem. At least it does not hurt that much. It certainly should make preserving missingness unnecessary.
As a practical matter, if you have large number of input features, you probably cannot have a 'good' strategy to impute after all. From the sheer imputation perspective, the best practice is anything but univariate. Which is in the contest of RF pretty much means to use the RF to impute before modeling with it. 
Therefore, unless somebody tells me (or us), "we are not able to do that", I think we should enable carrying forward missing 'cells', entirely bypassing the subject of how 'best' to impute.
Is it possible to have missing values in scikit-learn ? How should they be represented? I couldn't find any documentation about that.