OrdinalEncoder is the equivalent of pd.factorize and will work in the scikit-learn ecosystem.
However, be aware that you should not just swap OneHotEncoder to OrdinalEncoder just at your wish. It depends of your machine learning pipeline. As mentioned by Gael, tree-based algorithm will be fine with OrdinalEncoder. If you have a linear model, then you need to use the OneHotEncoder if the categories do not have any order. I will just refer to one notebook that we taught in EuroScipy last year: https://github.com/lesteve/euroscipy-2019-scikit-learn-tutorial/blob/master/rendered_notebooks/02_basic_preprocessing.ipynb On Fri, 1 May 2020 at 05:11, C W <tmrs...@gmail.com> wrote: > Hermes, > > That's an interesting function. Does it work with sklearn after > factorize? Is there any example? Thanks! > > On Thu, Apr 30, 2020 at 6:51 PM Hermes Morales <paisanoher...@hotmail.com> > wrote: > >> Perhaps pd.factorize could hello? >> >> Obtener Outlook para Android <https://aka.ms/ghei36> >> >> ------------------------------ >> *From:* scikit-learn <scikit-learn-bounces+paisanohermes= >> hotmail....@python.org> on behalf of Gael Varoquaux < >> gael.varoqu...@normalesup.org> >> *Sent:* Thursday, April 30, 2020 5:12:06 PM >> *To:* Scikit-learn mailing list <scikit-learn@python.org> >> *Subject:* Re: [scikit-learn] Why does sklearn require one-hot-encoding >> for categorical features? Can we have a "factor" data type? >> >> On Thu, Apr 30, 2020 at 03:55:00PM -0400, C W wrote: >> > I've used R and Stata software, none needs such transformation. They >> have a >> > data type called "factors", which is different from "numeric". >> >> > My problem with OHE: >> > One-hot-encoding results in large number of features. This really blows >> up >> > quickly. And I have to fight curse of dimensionality with PCA >> reduction. That's >> > not cool! >> >> Most statistical models still not one-hot encoding behind the hood. So, R >> and stata do it too. >> >> Typically, tree-based models can be adapted to work directly on >> categorical data. Ours don't. It's work in progress. >> >> G >> _______________________________________________ >> scikit-learn mailing list >> scikit-learn@python.org >> >> https://nam04.safelinks.protection.outlook.com/?url=https%3A%2F%2Fmail.python.org%2Fmailman%2Flistinfo%2Fscikit-learn&data=02%7C01%7C%7Ce7aa6f99b7914a1f84b208d7ed430801%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637238744453345410&sdata=e3BfHB4v5VFteeZ0Zh3FJ9Wcz9KmkUwur5i8Reue3mc%3D&reserved=0 >> _______________________________________________ >> 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 Scikit-learn @ Inria Foundation https://glemaitre.github.io/
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