I think Raghav is working on it in this PR:
The reason they weren't initially supported is likely that it involves a
lot of work and design choices to handle missing values appropriately, and
the discussion on the best way to handle it was postponed until there was a
working estimator which could serve most peoples needs.
On Thu, Oct 13, 2016 at 11:14 AM, Stuart Reynolds <stu...@stuartreynolds.net
> 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)
> - Stuart
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