Ahh, you are right. Regression vs. Classification is about the type of
target variable, not features.
Thanks, more clear now.
Mike
On Sat, Sep 14, 2019 at 1:23 AM Sebastian Raschka <m...@sebastianraschka.com>
wrote:
> 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!
> > >
> > >
> > >
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> >
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