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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