On Sat, 14 Sep 2019 at 20:59, C W <tmrs...@gmail.com> wrote: > 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. >
It would be interesting for us which part of the pipeline is tedious to set up to know if we can improve something there. Do you mean, that you would like to automatically detect of which type of feature (categorical/numerical) and apply a default encoder/scaling such as discuss there: https://github.com/scikit-learn/scikit-learn/issues/10603#issuecomment-401155127 IMO, one a user perspective, it would be cleaner in some cases at the cost of applying blindly a black box which might be dangerous. > > 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 >> > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn > -- Guillaume Lemaitre INRIA Saclay - Parietal team Center for Data Science Paris-Saclay https://glemaitre.github.io/
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