Please define “sensibly”. I would be strongly opposed to modifying any models to incorporate “missingness”. No model handles missing data for you. That is for you to decide based on your individual problem domain.
Take a look at a talk from last winter on missing data by Nina Zumel. Nina defines “sensibly” in several ways. https://www.r-bloggers.com/prepping-data-for-analysis-using-r/ __________________________________________________________________________________________ Dale Smith | Macy's Systems and Technology | IFS eCommerce | Data Science 770-658-5176 | 5985 State Bridge Road, Johns Creek, GA 30097 | dale.t.sm...@macys.com From: scikit-learn [mailto:scikit-learn-bounces+dale.t.smith=macys....@python.org] On Behalf Of Stuart Reynolds Sent: Thursday, October 13, 2016 2:14 PM To: scikit-learn@python.org Subject: [scikit-learn] Missing data and decision trees ⚠ EXT MSG: 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 * This is an EXTERNAL EMAIL. Stop and think before clicking a link or opening attachments.
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