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
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