I think Raghav is working on it in this PR: https://github.com/scikit-learn/scikit-learn/pull/5974
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 > wrote: > 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 > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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