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.
Dale Smith | Macy's Systems and Technology | IFS eCommerce | Data Science
770-658-5176 | 5985 State Bridge Road, Johns Creek, GA 30097 |
[mailto:scikit-learn-bounces+dale.t.smith=macys....@python.org] On Behalf Of
Sent: Thursday, October 13, 2016 2:14 PM
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)
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