Hi,
We're delighted to announce that Adam (https://github.com/adam2392) has
joined us as a new maintainer. He's been working on several aspects of the
project, including the tree code base, and we're very happy to have him on
board.
Regards,
Adrin
We are excited to welcome Yao Xiao (https://github.com/Charlie-XIAO) as a
core contributor of the scikit-learn project.
Your past contributions are greatly appreciated, and I'm looking forward to
working further with you.
On behalf of the scikit-learn team.
--
Guillaume Lemaitre
Open source engineer at :probabl.
Hi,
Excited to be on board and make scikit-learn and open source even better
and more exciting :).
On Tue, Jul 9, 2024 at 12:03 PM <scikit-learn-request(a)python.org> wrote:
> Send scikit-learn mailing list submissions to
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> than "Re: Contents of scikit-learn digest..."
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>
> Today's Topics:
>
> 1. Welcome Adam Li as a new maintainer (Adrin)
> 2. Re: Welcome Adam Li as a new maintainer (Gael Varoquaux)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 9 Jul 2024 10:41:43 +0200
> From: Adrin <adrin.jalali(a)gmail.com>
> To: Scikit-learn mailing list <scikit-learn(a)python.org>
> Subject: [scikit-learn] Welcome Adam Li as a new maintainer
> Message-ID:
> <CAEOrW4-9vv6p=phFyN=
> 7W-f46ByMPM4SoEMJkGm7SDL3fgV5SQ(a)mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Hi,
>
> We're delighted to announce that Adam (https://github.com/adam2392) has
> joined us as a new maintainer. He's been working on several aspects of the
> project, including the tree code base, and we're very happy to have him on
> board.
>
> Regards,
> Adrin
>
U
On Wed, Jul 3, 2024, 21:30 <scikit-learn-request(a)python.org> wrote:
> Send scikit-learn mailing list submissions to
> scikit-learn(a)python.org
>
> To subscribe or unsubscribe via the World Wide Web, visit
> https://mail.python.org/mailman/listinfo/scikit-learn
> or, via email, send a message with subject or body 'help' to
> scikit-learn-request(a)python.org
>
> You can reach the person managing the list at
> scikit-learn-owner(a)python.org
>
> When replying, please edit your Subject line so it is more specific
> than "Re: Contents of scikit-learn digest..."
>
>
> Today's Topics:
>
> 1. [ANN] scikit-learn 1.5.1 is online! (J?r?mie du Boisberranger)
> 2. Re: [ANN] scikit-learn 1.5.1 is online! (Guillaume Lema?tre)
> 3. Skrub 0.2.0: tabular learning made easy (Gael Varoquaux)
> 4. Re: [ANN] scikit-learn 1.5.1 is online! (Gael Varoquaux)
>
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Wed, 3 Jul 2024 11:23:17 +0200
> From: J?r?mie du Boisberranger <jeremie.du-boisberranger(a)inria.fr>
> To: scikit-learn(a)python.org
> Subject: [scikit-learn] [ANN] scikit-learn 1.5.1 is online!
> Message-ID: <e0ef2dd6-723b-4f9b-8e9d-1593665b3d97(a)inria.fr>
> Content-Type: text/plain; charset=UTF-8; format=flowed
>
> Hello everyone,
>
> We're happy to announce the 1.5.1 release !
>
>
> It contains fixes for a few regressions introduced in 1.5.
>
> You can see the changelog here:
> https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1
>
>
> You can upgrade with pip as usual:
>
> ```
> pip install -U scikit-learn
> ```
>
> The conda-forge builds can be installed using:
>
> ```
> conda install -c conda-forge scikit-learn
> ```
>
>
> Thanks to everyone who contributed to this release !
>
> J?r?mie, on behalf of the Scikit-learn maintainers team.
>
>
>
> ------------------------------
>
> Message: 2
> Date: Wed, 3 Jul 2024 11:28:31 +0200
> From: Guillaume Lema?tre <g.lemaitre58(a)gmail.com>
> To: Scikit-learn mailing list <scikit-learn(a)python.org>
> Subject: Re: [scikit-learn] [ANN] scikit-learn 1.5.1 is online!
> Message-ID:
> <CACDxx9hovH3CydHOkOAQ1yWXBJJqntkF5pt+OjL6Lx=
> xUtTpiw(a)mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> Thanks J?r?mie for this one.
>
> On Wed, 3 Jul 2024 at 11:26, J?r?mie du Boisberranger <
> jeremie.du-boisberranger(a)inria.fr> wrote:
>
> > Hello everyone,
> >
> > We're happy to announce the 1.5.1 release !
> >
> >
> > It contains fixes for a few regressions introduced in 1.5.
> >
> > You can see the changelog here:
> > https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1
> >
> >
> > You can upgrade with pip as usual:
> >
> > ```
> > pip install -U scikit-learn
> > ```
> >
> > The conda-forge builds can be installed using:
> >
> > ```
> > conda install -c conda-forge scikit-learn
> > ```
> >
> >
> > Thanks to everyone who contributed to this release !
> >
> > J?r?mie, on behalf of the Scikit-learn maintainers team.
> >
> > _______________________________________________
> > scikit-learn mailing list
> > scikit-learn(a)python.org
> > https://mail.python.org/mailman/listinfo/scikit-learn
> >
>
>
> --
> Guillaume Lemaitre
> Open source engineer at :probabl.
>
Hi scikit-learn'ers
We just released skrub 0.2.0: https://skrub-data.org. This release markedly simplifies learning on complex dataframes.
# `model = tabular_learner('classifier')`
The highlight of the release is the `tabular_learner` function, which facilitates creating pipelines that readily perform machine learning on dataframes, adding preprocessing to a scikit-learn compatible learner. The function basically packs defaults and heuristics to transform all forms of dataframes to a representation that is well suited to a learner, and it can adapt these transformation: tabular_learner(HistGradientBoostingClassifier()) encodes categories differently than tabular_learner(LogisticRegression()).
The heuristics are tuned based on much benchmarking and experience shows that they give good tradeoffs. The default `tabular_learner('classifier')` is often a strong baseline.
# `transformer = TableVectorizer()`
Behind the hood, the work is done by the `skrub.TableVectorizer()`, a scikit-learn compatible transformer that facilitates combining multiple transformations on the different columns of a dataframe. The TableVectorizer is not new in the 0.2.0 release, but we have completely revamped its internals to cover really well edge cases. Indeed, one challenge is to make sure that nothing different or strange happens at test time. Actually, enforcing consistency between train-time and test-time transformation is the real value of skrub compared to using pandas or polars to do transformation.
# Increasing support of polars
We have implemented a new mechanism for supporting both pandas and polars. It has not been applied on all the codebase, hence the support is still imperfect. However, we are seeing increasing support for polars in skrub, and our goal in the short term is to provide rock-solid polar support.
Try skrub out! It's still young, but in mind opinion, it provides a lot of value to tabular learning.
Cheers,
Gaël
Hello everyone,
We're happy to announce the 1.5.1 release !
It contains fixes for a few regressions introduced in 1.5.
You can see the changelog here:
https://scikit-learn.org/stable/whats_new/v1.5.html#version-1-5-1
You can upgrade with pip as usual:
```
pip install -U scikit-learn
```
The conda-forge builds can be installed using:
```
conda install -c conda-forge scikit-learn
```
Thanks to everyone who contributed to this release !
Jérémie, on behalf of the Scikit-learn maintainers team.
