[scikit-learn] Can Scikit-learn decision tree (CART) have both continuous and categorical features?
Sebastian Raschka
mail at sebastianraschka.com
Fri Oct 4 23:28:46 EDT 2019
The docs show a way such that you don't need to write it as png file using tree.plot_tree:
https://scikit-learn.org/stable/modules/tree.html#classification
I don't remember why, but I think I had problems with that in the past (I think it didn't look so nice visually, but don't remember), which is why I still stick to graphviz. For my use cases, it's not much hassle -- it used to be a bit of a hassle to get GraphViz working, but now you can do
conda install pydotplus
conda install graphviz
Coincidentally, I just made an example for a lecture I was teaching on Tue: https://github.com/rasbt/stat479-machine-learning-fs19/blob/master/06_trees/code/06-trees_demo.ipynb
Best,
Sebastian
> On Oct 4, 2019, at 10:09 PM, C W <tmrsg11 at gmail.com> wrote:
>
> On a separate note, what do you use for plotting?
>
> I found graphviz, but you have to first save it as a png on your computer. That's a lot work for just one plot. Is there something like a matplotlib?
>
> Thanks!
>
> On Fri, Oct 4, 2019 at 9:42 PM Sebastian Raschka <mail at sebastianraschka.com> wrote:
> Yeah, think of it more as a computational workaround for achieving the same thing more efficiently (although it looks inelegant/weird)-- something like that wouldn't be mentioned in textbooks.
>
> Best,
> Sebastian
>
> > On Oct 4, 2019, at 6:33 PM, C W <tmrsg11 at gmail.com> wrote:
> >
> > Thanks Sebastian, I think I get it.
> >
> > It's just have never seen it this way. Quite different from what I'm used in Elements of Statistical Learning.
> >
> > On Fri, Oct 4, 2019 at 7:13 PM Sebastian Raschka <mail at sebastianraschka.com> wrote:
> > 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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