Sure, I just ran an example I made with graphviz via plot_tree, and it looks like there's an issue with overlapping boxes if you use class (and/or feature) names. I made a reproducible example here so that you can take a look: https://github.com/rasbt/bugreport/blob/master/scikit-learn/plot_tree/tree-demo-1.ipynb
Happy to add this to the sklearn issue list if there's no issue filed for that yet. Best, Sebastian > On Oct 6, 2019, at 9:10 AM, Andreas Mueller <t3k...@gmail.com> wrote: > > > > On 10/4/19 11:28 PM, Sebastian Raschka wrote: >> 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. > Can you give me examples that don't look as nice? I would love to improve it. > >> 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 <tmrs...@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 >>> <m...@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 <tmrs...@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 >>>> <m...@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 <tmrs...@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 >>>>> <m...@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 <nio...@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 >>>>>>> <m...@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 <tmrs...@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 <t3k...@gmail.com> >>>>>>>> wrote: >>>>>>>> >>>>>>>> >>>>>>>> On 9/15/19 8:16 AM, Guillaume Lemaître wrote: >>>>>>>>> >>>>>>>>> On Sat, 14 Sep 2019 at 20:59, C W <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> 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! >>>>>>>>> >>>>>>>>> >>>>>>>>> >>>>>>>>> _______________________________________________ >>>>>>>>> scikit-learn mailing list >>>>>>>>> scikit-learn@python.org >>>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>>> _______________________________________________ >>>>>>>>> scikit-learn mailing list >>>>>>>>> scikit-learn@python.org >>>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>>> _______________________________________________ >>>>>>>>> scikit-learn mailing list >>>>>>>>> scikit-learn@python.org >>>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>>> >>>>>>>>> >>>>>>>>> -- >>>>>>>>> Guillaume Lemaitre >>>>>>>>> INRIA Saclay - Parietal team >>>>>>>>> Center for Data Science Paris-Saclay >>>>>>>>> https://glemaitre.github.io/ >>>>>>>>> >>>>>>>>> >>>>>>>>> _______________________________________________ >>>>>>>>> scikit-learn mailing list >>>>>>>>> >>>>>>>>> scikit-learn@python.org >>>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>> _______________________________________________ >>>>>>>> scikit-learn mailing list >>>>>>>> scikit-learn@python.org >>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>>> _______________________________________________ >>>>>>>> scikit-learn mailing list >>>>>>>> scikit-learn@python.org >>>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>> _______________________________________________ >>>>>>> scikit-learn mailing list >>>>>>> scikit-learn@python.org >>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>>> >>>>>>> >>>>>>> _______________________________________________ >>>>>>> scikit-learn mailing list >>>>>>> >>>>>>> scikit-learn@python.org >>>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>>> _______________________________________________ >>>>>> scikit-learn mailing list >>>>>> scikit-learn@python.org >>>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> scikit-learn@python.org >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>>> _______________________________________________ >>>>> scikit-learn mailing list >>>>> scikit-learn@python.org >>>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>>> _______________________________________________ >>>> scikit-learn mailing list >>>> scikit-learn@python.org >>>> https://mail.python.org/mailman/listinfo/scikit-learn >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >>> _______________________________________________ >>> scikit-learn mailing list >>> scikit-learn@python.org >>> https://mail.python.org/mailman/listinfo/scikit-learn >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> https://mail.python.org/mailman/listinfo/scikit-learn > > _______________________________________________ > scikit-learn mailing list > scikit-learn@python.org > https://mail.python.org/mailman/listinfo/scikit-learn _______________________________________________ scikit-learn mailing list scikit-learn@python.org https://mail.python.org/mailman/listinfo/scikit-learn