[scikit-learn] Can Scikit-learn decision tree (CART) have both continuous and categorical features?
Sebastian Raschka
mail at sebastianraschka.com
Fri Oct 4 18:50:41 EDT 2019
Not sure if there's a website for that. In any case, to explain this differently, as discussed earlier sklearn assumes continuous features for decision trees. So, it will use a binary threshold for splitting along a feature attribute. In other words, it cannot do sth like
if x == 1 then right child node
else left child node
Instead, what it does is
if x >= 0.5 then right child node
else left child node
These are basically equivalent as you can see when you just plug in values 0 and 1 for x.
Best,
Sebastian
> On Oct 4, 2019, at 5:34 PM, C W <tmrsg11 at gmail.com> wrote:
>
> I don't understand your answer.
>
> Why after one-hot-encoding it still outputs greater than 0.5 or less than? Does sklearn website have a working example on categorical input?
>
> Thanks!
>
> On Fri, Oct 4, 2019 at 3:48 PM Sebastian Raschka <mail at sebastianraschka.com> wrote:
> Like Nicolas said, the 0.5 is just a workaround but will do the right thing on the one-hot encoded variables, here. You will find that the threshold is always at 0.5 for these variables. I.e., what it will do is to use the following conversion:
>
> treat as car_Audi=1 if car_Audi >= 0.5
> treat as car_Audi=0 if car_Audi < 0.5
>
> or, it may be
>
> treat as car_Audi=1 if car_Audi > 0.5
> treat as car_Audi=0 if car_Audi <= 0.5
>
> (Forgot which one sklearn is using, but either way. it will be fine.)
>
> Best,
> Sebastian
>
>
>> On Oct 4, 2019, at 1:44 PM, Nicolas Hug <niourf at gmail.com> wrote:
>>
>>
>>> But, decision tree is still mistaking one-hot-encoding as numerical input and split at 0.5. This is not right. Perhaps, I'm doing something wrong?
>>
>> You're not doing anything wrong, and neither is the tree. Trees don't support categorical variables in sklearn, so everything is treated as numerical.
>>
>> This is why we do one-hot-encoding: so that a set of numerical (one hot encoded) features can be treated as if they were just one categorical feature.
>>
>>
>>
>> Nicolas
>>
>> On 10/4/19 2:01 PM, C W wrote:
>>> Yes, you are right. it was 0.5 and 0.5 for split, not 1.5. So, typo on my part.
>>>
>>> Looks like I did one-hot-encoding correctly. My new variable names are: car_Audi, car_BMW, etc.
>>>
>>> But, decision tree is still mistaking one-hot-encoding as numerical input and split at 0.5. This is not right. Perhaps, I'm doing something wrong?
>>>
>>> Is there a good toy example on the sklearn website? I am only see this: https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html.
>>>
>>> Thanks!
>>>
>>>
>>>
>>> On Fri, Oct 4, 2019 at 1:28 PM Sebastian Raschka <mail at sebastianraschka.com> wrote:
>>> 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> 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> 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 <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/
>>>>>
>>>>> On Sat, Sep 14, 2019 at 3:40 AM C W <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, 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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