Name: PyData Paris - March 2024 Meetup
Start: 2024-03-21T19:00:00+01:00
End: 2024-03-21T21:00:00+01:00
Location: Iliad

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](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.

Sylvain Corlay

Sandrine Pataut

PyData Paris

PyData

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