Thanks, Guillaume. Column transformer looks pretty neat. I've also heard though, this pipeline can be tedious to set up? Specifying what you want for every feature is a pain.
Jaiver, Actually, you guessed right. My real data has only one numerical variable, looks more like this: Gender Date Income Car Attendance Male 2019/3/01 10000 BMW Yes Female 2019/5/02 9000 Toyota No Male 2019/7/15 12000 Audi Yes I am predicting income using all other categorical variables. Maybe it is catboost! Thanks, M On Sat, Sep 14, 2019 at 9:25 AM Javier López <jlo...@ende.cc> wrote: > 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 >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn >
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