[scikit-learn] Why does sklearn require one-hot-encoding for categorical features? Can we have a "factor" data type?

[C W]

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 <paisanohermes at hotmail.com>
wrote:

> Perhaps pd.factorize could hello?
>
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> ------------------------------
> *From:* scikit-learn <scikit-learn-bounces+paisanohermes=
> hotmail.com at python.org> on behalf of Gael Varoquaux <
> gael.varoquaux at normalesup.org>
> *Sent:* Thursday, April 30, 2020 5:12:06 PM
> *To:* Scikit-learn mailing list <scikit-learn at 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
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