[scikit-learn] Missing data and decision trees

[Stuart Reynolds]

I'm looking for a decision tree and RF implementation that supports missing
data (without imputation) -- ideally in Python, Java/Scala or C++.

It seems that scikit's decision tree algorithm doesn't allow this -- which
is disappointing because its one of the few methods that should be able to
sensibly handle problems with high amounts of missingness.

Are there plans to allow missing data in scikit's decision trees?

Also, is there any particular reason why missing values weren't supported
originally (e.g. integrates poorly with other features)

Regards
- Stuart
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