If you have datasets with many categorical features, and perhaps many categories, the tools in sklearn are quite limited, but there are alternative implementations of boosted trees that are designed with categorical features in mind. Take a look at catboost [1], which has an sklearn-compatible API.
J [1] https://catboost.ai/ On Sat, Sep 14, 2019 at 3:40 AM C W <tmrs...@gmail.com> wrote: > Hello all, > I'm very confused. Can the decision tree module handle both continuous and > categorical features in the dataset? In this case, it's just CART > (Classification and Regression Trees). > > For example, > Gender Age Income Car Attendance > Male 30 10000 BMW Yes > Female 35 9000 Toyota No > Male 50 12000 Audi Yes > > According to the documentation > https://scikit-learn.org/stable/modules/tree.html#tree-algorithms-id3-c4-5-c5-0-and-cart, > it can not! > > It says: "scikit-learn implementation does not support categorical > variables for now". > > Is this true? If not, can someone point me to an example? If yes, what do > people do? > > Thank you very much! > > > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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