Hi Mike,
just to make sure we are on the same page,
> I have mixed data type (continuous and categorical). Should I
> tree.DecisionTreeClassifier() or tree.DecisionTreeRegressor()?
that's independent from the previous email. The comment
> > "scikit-learn implementation does not support categorical variables for
> > now".
we discussed via the previous email was referring to feature variables. Whether
you choose the DT regressor or classifier depends on the format of your target
variable.
Best,
Sebastian
> On Sep 13, 2019, at 11:41 PM, C W <tmrs...@gmail.com> wrote:
>
> Thanks, Sebastian. It's great to know that it works, just need to do
> one-hot-encoding first.
>
> I have mixed data type (continuous and categorical). Should I
> tree.DecisionTreeClassifier() or tree.DecisionTreeRegressor()?
>
> I'm guessing tree.DecisionTreeClassifier()?
>
> Best,
>
> Mike
>
> On Fri, Sep 13, 2019 at 11:59 PM Sebastian Raschka
> <m...@sebastianraschka.com> wrote:
> Hi,
>
> if you have the category "car" as shown in your example, this would
> effectively be something like
>
> BMW=0
> Toyota=1
> Audi=2
>
> Sure, the algorithm will execute just fine on the feature column with values
> in {0, 1, 2}. However, the problem is that it will come up with binary rules
> like x_i>= 0.5, x_i>= 1.5, and x_i>= 2.5. I.e., it will treat it is a
> continuous variable.
>
> What you can do is to encode this feature via one-hot encoding -- basically
> extend it into 2 (or 3) binary variables. This has it's own problems (if you
> have a feature with many possible values, you will end up with a large number
> of binary variables, and they may dominate in the resulting tree over other
> feature variables).
>
> In any case, I guess this is what
>
> > "scikit-learn implementation does not support categorical variables for
> > now".
>
>
> means ;).
>
> Best,
> Sebastian
>
> > On Sep 13, 2019, at 9:38 PM, 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
_______________________________________________
scikit-learn mailing list
scikit-learn@python.org
https://mail.python.org/mailman/listinfo/scikit-learn
