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> >>>> <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 >>> listscikit-learn@python.orghttps://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 > listscikit-learn@python.orghttps://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 >
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