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
