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
wrote:
> Hi Mike,
>
> just to make sure we are on the same page,
>
> > I have mixed data type (continuous and cat
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 catego
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
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,
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 1 BMW Yes
Female 35 9000
I don't know what is the policy about a sklearn 1.0 w.r.t api changes.
If it's meant to be a special release with possible api changes without
deprecation cycles, I think this change is a good candidate for 1.0
Otherwise I'm +1 and agree with Guillaume, people will get used to it by
using it
