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PyLadies Berlin are excited to bring you this open source workshop dedicated to setting up your scikit-learn or fairlearn development environment.

scikit-learn is a popular machine learning library and is widely adopted in industry as well as academia.

Fairlearn is a community-driven project to help data scientists improve fairness of AI systems.

This is a warm-up session for the upcoming workshop, but it is open to anyone who would like to get guidance, even if you won’t attend the workshop. No prior contributing experience required!

By setting up your development environment in advance, you can use the in-person workshop time for finding an issue to work on, and working on a contribution.

• Format for the session: First 10 minutes : welcome and introduction The rest : "office hours" during which you can ask questions and where you'll be supported with setting up a development environment.

• Preparation work for scikit-learn To get the most out of the session, it's encouraged that you check out the Developer's Guide of scikit-learn and follow steps 1 to 7 under the section "How to contribute": https://scikit-learn.org/dev/developers/contributing.html#how-to-contribute. You will also find a lot of useful additional information on this page, for example video resources: https://scikit-learn.org/dev/developers/contributing.html#video-resources. Please be aware that it could take longer to set up a development environment on ​​a computer running a Windows operating system compared to MacOS or Unix. If you are using Windows, it is recommended to install Windows Subsystem for Linux. You can find instructions on the installation process for example by following the steps described here: https://learn.microsoft.com/en-us/windows/wsl/install. Please note that you should be using WSL 2, and here's how you can upgrade to this version if you have WSL 1: https://learn.microsoft.com/en-us/windows/wsl/install#upgrade-version-from-wsl-1-to-wsl-2

• Preparation work for Fairlearn You don't need to do any preparation work for Fairlearn for this session, instructions will be given during this event. However, it is recommended that you have a look at the Fairlearn Contributor Guide here: https://fairlearn.org/v0.12/contributor_guide/index.html

• How to join We'll be using Discord for this event. Please follow the steps below: 1. Join the scikit-learn Discord server: https://discord.gg/aBgkfXBtWZ 2. Join the #help-desk-voice channel. This is the channel where we'll be hosting this workshop.

• Audience level Everyone is welcome to attend this session! If you've never contributed to open source software before, then you will learn how to, and if you have experience contributing, then you can either help mentor other attendees or you can work on more challenging contributions. It is useful to have some scikit-learn, git, and python experience.

• Facilitators The session will be lead by Maren Westermann (PyLadies Berlin, scikit-learn team member), Tamara Atanasoska (scikit-learn contributor, Fairlearn maintainer), Stefanie Senger (scikit-learn team member), Adrin Jalali (scikit-learn and Fairlearn maintainer) and Guillaume Lemaitre (scikit-learn maintainer).

• By attending our event, you agree to the PyLadies Code of Conduct: https://www.pyladies.com/CodeOfConduct/

❓ Can men attend ❓ Everyone is welcome. If you identify as someone well-represented in open source and in tech, please be mindful of the space and privileges you have, and use it to support others.

• Contact Interested in speaking at one of our events? Have a good idea for a Meetup? Get in touch with us at [email protected] Find us on the PyLadies Global workspace: 1. https://slackin.pyladies.com enter your email address. 2. Accept the email invitation 3. Go to workspace https://pyladies.slack.com 4. Join channel #city-berlin\, #germany\, #jobs-europe

Mark your calendar for the next session of the PyData Paris Meetup, on March 21st 2024. This Meetup will be hosted by Scaleway, Europe's empowering cloud provider at the Iliad group office, 16 rue de la ville l'evêque 75008 Paris. The speakers for this session, that will be dedicated to Taipy are Alexandre Sajus and Florian Jacta.

Schedule 7:00pm - 7:15pm: Community announcements & short address by Fred Bardolle, Lead Product Manager AI at Scaleway. 7:15pm - 7:45pm: Get the best from your scikit-learn classifier: trusted probabilities and optimal binary decision, Guillaume Lemaître 7:45pm - 8:30pm: Deploy your Data Project on the Web using only Python, Alexandre Sajus & Florian Jacta 8:30pm - 9:30pm: Buffet

Speakers

Alexandre Sajus is a customer success engineer at Taipy. He graduated with a Master's of Engineering from Centrale Paris. Florian Jacta is a data scientist and community manager at Taipy.

Guillaume Lemaitre is an open-source scientific software developer at :probabl. and a core developer of the scikit-learn project.

Abstracts

Deploy your Data Project on the Web using only Python, Alexandre Sajus & Florian Jacta In the Python ecosystem, many packages are available for running algorithms, training models, and visualizing data. Despite this, over 85% of data science projects stay at the proof-of-concept stage and never reach the production stage. With Taipy, Python developers can build great pilots as well as stunning production-ready web applications designed for end-users.

Get the best from your scikit-learn classifier: trusted probabilties and optimal binary decision, Guillaume Lemaitre When operating a classifier in a production setting (i.e. predictive phase), practitioners are interested in potentially two different outputs: a "hard" decision used to leverage a business decision or/and a "soft" decision to get a confidence score linked to each potential decision (e.g. usually related to class probabilities). Scikit-learn does not provide any flexibility to go from "soft" to "hard" predictions: it uses a cut-off point at a confidence score of 0.5 (or 0 when using decision_function) to get class labels. However, optimizing a classifier to get a confidence score close to the true probabilities (i.e. a calibrated classifier) does not guarantee to obtain accurate "hard" predictions using this heuristic. Reversely, training a classifier for an optimum "hard" prediction accuracy (with the cut-off constraint at 0.5) does not guarantee obtaining a calibrated classifier. In this talk, we will present a new scikit-learn meta-estimator allowing us to get the best of the two worlds: a calibrated classifier providing optimum "hard" predictions. This meta-estimator will land in a future version of scikit-learn: https://github.com/scikit-learn/scikit-learn/pull/26120. We will provide some insights regarding the way to obtain accurate probabilities and predictions and also illustrate how to use in practice this model on different use cases: cost-sensitive problems and imbalanced classification problems.

PyLadies Berlin are excited to bring you this open source workshop dedicated to contributing to scikit-learn!

scikit-learn is a popular machine learning library and is widely adopted in industry as well as academia. In this session, you will be guided on how you can make your own contributions to the project, no prior experience in contributing required! Not only will this teach you new skills and boost your CV, you'll also likely get a nice adrenaline rush when your contribution is accepted! If you don’t finish your contribution during the event, we hope you will continue to work on it after the workshop. PyLadies Berlin also offers regular open source hack events where you can continue to meet and work on your contributions together.

• Format for the session: 6:30 - 6:45 pm: Welcome, networking, food and drinks 6:45 - 6:50 pm Community announcements 6:50 - 7:00 pm: Introduction to scikit-learn - what you can contribute and how to contribute from 7:00 pm: "Office hours" during which you'll be mentored for making a contribution to scikit-learn.

**Pre Workshop Session** For attending the event it is required that you have successfully installed the dev version of scikit-learn, have set up a dedicated python environment for the project, and that tests are running successfully. We have a dedicated pre-workshop event scheduled for these tasks which will be happening on 27 July. It is mandatory to sign up for this event if you don't have a working development environment. You can install all requirements yourself by following the instructions on this repository under "1.environment.md" and "2.building.md": https://github.com/scikit-learn-inria-fondation/EuroSciPy22 . You will also find a lot of useful additional information on this page. Note that during the main workshop, we won't have the resources to help you set up your environment.

❓ Can men attend ❓ Everyone is welcome. :) If you identify as someone well-represented in open source and in tech, please be mindful of the space and privileges you have, and use it to support others.

• Audience level Everyone is welcome to attend this session! If you've never contributed to open source software before, then you will learn how to, and if you have experience contributing, then you can either help mentor other attendees or you can work on more challenging contributions. It is useful to have some scikit-learn, git, and python experience.

• Facilitators The session will be lead by Maren Westermann (PyLadies Berlin, scikit-learn contributor experience team), Adrin Jalali (scikit-learn maintainer), and Guillaume Lemaitre (scikit-learn maintainer), Stefanie Senger (scikit-learn intern), and Noa Tamir (PyLadies Berlin, pandas maintainer)

• Host This event is being sponsored by DB Systel GmbH Food and drinks will be available by our hosts 🥳

• By attending our event, you agree to the PyLadies Code of Conduct: https://www.pyladies.com/CodeOfConduct/

• Contact Interested in speaking at one of our events? Have a good idea for a Meetup? Get in touch with us at [email protected] Find us on the PyLadies Global workspace: 1. https://slackin.pyladies.com enter your email address. 2. Accept the email invitation 3. Go to workspace https://pyladies.slack.com 4. Join channel #city-berlin\, #germany\, #jobs-europe

PyLadies Berlin are excited to bring you this open source workshop dedicated to setting up your scikit-learn development environment.

scikit-learn is a popular machine learning library and is widely adopted in industry as well as academia. This is a warm-up session for the upcoming workshop, but it is open to anyone who would like to get guidance, even if you won’t attend the workshop. No prior contributing experience required!

By setting up your development environment in advance, you can use the in-person workshop time for finding an issue to work on, and working on a contribution.

In addition, PyLadies Berlin offers regular open source hack events where you can continue to meet and work on your contributions together.

• Format for the session: First 10 minutes : welcome and introduction The rest : "office hours" during which you can ask questions and where you'll be supported with setting up a development environment.

• Preparation work To get the most out of the session, it's encouraged that you check out the following repository and follow the instructions under "1.environment.md" and "2.building.md": https://github.com/scikit-learn-inria-fondation/EuroSciPy22 . You will also find a lot of useful additional information on this page. Please be aware that it could take longer to set up a development environment on ​​a computer running a Windows operating system compared to MacOS or Unix. If you are using Windows, it is recommended to install Windows Subsystem for Linux. You can find instructions on the installation process for example by following the steps described here: https://learn.microsoft.com/en-us/windows/wsl/install Please note that you should be using WSL 2, and here's how you can upgrade to this version if you have WSL 1: https://learn.microsoft.com/en-us/windows/wsl/install#upgrade-version-from-wsl-1-to-wsl-2

• How to join We'll be using Discord for this event. Please follow the steps below: 1. Join the scikit-learn Discord server: https://discord.com/invite/4GbqW7vpbS 2. Join the #help-desk-voice channel. This is the channel where we'll be hosting this workshop.

• Audience level Everyone is welcome to attend this session! If you've never contributed to open source software before, then you will learn how to, and if you have experience contributing, then you can either help mentor other attendees or you can work on more challenging contributions. It is useful to have some scikit-learn, git, and python experience.

• Facilitators The session will be led by Maren Westermann (PyLadies Berlin, scikit-learn contributor experience team), Adrin Jalali (scikit-learn maintainer), and Guillaume Lemaitre (scikit-learn maintainer), and Stefanie Senger (scikit-learn intern).

• By attending our event, you agree to the PyLadies Code of Conduct: https://www.pyladies.com/CodeOfConduct/

❓ Can men attend ❓ Everyone is welcome. :) If you identify as someone well-represented in open source and in tech, please be mindful of the space and privileges you have, and use it to support others.

• Contact Interested in speaking at one of our events? Have a good idea for a Meetup? Get in touch with us at [email protected] Find us on the PyLadies Global workspace: 1. https://slackin.pyladies.com enter your email address. 2. Accept the email invitation 3. Go to workspace https://pyladies.slack.com 4. Join channel #city-berlin\, #germany\, #jobs-europe