It's not a decision tree, but py-earth may also do what you need. It handles missingness as described in section 3.4 here: http://media.salford-systems.com/library/MARS_V2_JHF_LCS-108.pdf. Basically, missingness is considered potentially predictive.
On Thu, Oct 13, 2016 at 11:20 AM, Jeff <jeffrey.m.all...@gmail.com> wrote: > I ran into this several times as well with scikit-learn implementation of > GBM. Look at xgboost if you have not already (is there someone out there > that hasn't ? :)- it deals with missing values in the predictor space in a > very eloquent manner. > > http://xgboost.readthedocs.io/en/latest/python/python_intro.html > > https://arxiv.org/abs/1603.02754 > > > Jeff > > > > On 10/13/2016 2:14 PM, Stuart Reynolds 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 > listscikit-learn@python.orghttps://mail.python.org/mailman/listinfo/scikit-learn > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > >
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