[scikit-learn] Can Scikit-learn decision tree (CART) have both continuous and categorical features?

Sebastian Raschka mail at sebastianraschka.com
Fri Oct 4 13:03:17 EDT 2019


Hi,

> The funny part is: the tree is taking one-hot-encoding (BMW=0, Toyota=1, Audi=2) as numerical values, not category.The tree splits at 0.5 and 1.5

that's not a onehot encoding then.

For an Audi datapoint, it should be

BMW=0
Toyota=0
Audi=1

for BMW

BMW=1
Toyota=0
Audi=0

and for Toyota

BMW=0
Toyota=1
Audi=0

The split threshold should then be at 0.5 for any of these features.

Based on your email, I think you were assuming that the DT does the one-hot encoding internally, which it doesn't. In practice, it is hard to guess what is a nominal and what is a ordinal variable, so you have to do the onehot encoding before you give the data to the decision tree.

Best,
Sebastian

> On Oct 4, 2019, at 11:48 AM, C W <tmrsg11 at gmail.com> wrote:
> 
> I'm getting some funny results. I am doing a regression decision tree, the response variables are assigned to levels.
> 
> The funny part is: the tree is taking one-hot-encoding (BMW=0, Toyota=1, Audi=2) as numerical values, not category.
> 
> The tree splits at 0.5 and 1.5. Am I doing one-hot-encoding wrong? How does the sklearn know internally 0 vs. 1 is categorical, not numerical? 
> 
> In R for instance, you do as.factor(), which explicitly states the data type.
> 
> Thank you!
> 
> 
> On Wed, Sep 18, 2019 at 11:13 AM Andreas Mueller <t3kcit at gmail.com <mailto:t3kcit at gmail.com>> wrote:
> 
> 
> On 9/15/19 8:16 AM, Guillaume Lemaître wrote:
>> 
>> 
>> On Sat, 14 Sep 2019 at 20:59, C W <tmrsg11 at gmail.com <mailto:tmrsg11 at 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 <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 <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 <jlopez at ende.cc> <mailto:jlopez at 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/ <https://catboost.ai/>
>> On Sat, Sep 14, 2019 at 3:40 AM C W <tmrsg11 at gmail.com <mailto:tmrsg11 at 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 <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 at python.org <mailto:scikit-learn at python.org>
>> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org <mailto:scikit-learn at python.org>
>> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org <mailto:scikit-learn at python.org>
>> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>
>> 
>> 
>> -- 
>> Guillaume Lemaitre
>> INRIA Saclay - Parietal team
>> Center for Data Science Paris-Saclay
>> https://glemaitre.github.io/ <https://glemaitre.github.io/>
>> 
>> _______________________________________________
>> scikit-learn mailing list
>> scikit-learn at python.org <mailto:scikit-learn at python.org>
>> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>
> 
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org <mailto:scikit-learn at python.org>
> https://mail.python.org/mailman/listinfo/scikit-learn <https://mail.python.org/mailman/listinfo/scikit-learn>
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn

-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20191004/4b392719/attachment-0001.html>


More information about the scikit-learn mailing list