On 9/15/19 8:16 AM, Guillaume Lemaître wrote:
On Sat, 14 Sep 2019 at 20:59, C W <tmrs...@gmail.com
<mailto: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.
Also see
https://amueller.github.io/dabl/dev/generated/dabl.EasyPreprocessor.html#dabl.EasyPreprocessor
Which basically does that.
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
<mailto: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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--
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
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