Probabl maintains scikit-learn.
Our team builds and maintains robust machine learning algorithms while keeping them simple
and accessible, staying true to scikit-learn's founding philosophy of making predictive
data analysis tools efficient and reusable in any context.
Beyond scikit-learn,
we're expanding our impact across the entire data science pipeline: from where your data
lives, with skrub handling the messy reality of heterogeneous tables and dataframes, to
guiding you through the maze of experimentation with skore, helping data scientists move
faster from raw data to validated, production-ready models.
members of the scikit-learn core team employed by Probabl
Gael Varoquaux
Co-founder, CSO
Olivier Grisel
Co-founder, Core maintainer
Guillaume Lemaitre
Co-founder, Core maintainer
Jérémie du Boisberranger
Co-founder, Core maintainer
Adrin Jalali
Co-founder, Core maintainer
Loïc Estève
Co-founder, Core maintainer
François Goupil
Co-founder, Core contributor
Arturo Amor
Co-founder, Core contributor
Stefanie Senger
Core maintainer
From an open-source initiative to the world's most used ML library
Tools and expertise from the source
We build products that encode the methodology and best practices our maintainers
have
developed over 15 years. Every data scientist can benefit from that depth, from
day
one.
The Data Science platform by the scikit-learn founders
Skore
Evaluate any scikit-learn compatible model in one line of code. skore automatically generates the right metrics, feature importance plots, and diagnostics for your use case, with smart methodological warnings that catch common pitfalls before they reach production.
- Automated evaluation reports
- Cross-validation insights
- Model comparison
- Methodological guidance
import skore, skrub
from sklearn.linear_model import Ridge
model = skrub.tabular_pipeline(Ridge())
report = skore.evaluate(model, df, y, splitter=5)
reportimport skore, skrub
from sklearn.linear_model import Ridge
from sklearn.ensemble import RandomForestRegressor
models = [
skrub.tabular_pipeline(Ridge()),
skrub.tabular_pipeline(RandomForestRegressor()),
]
comparison_report = skore.evaluate(models, df, y, splitter=5)
comparison_reportThe official scikit-learn training and certification
Skolar
Hands-on courses built by scikit-learn core developers, the same people who created the MOOC that reached 40,000+ learners worldwide. Validate your ML expertise with the only official scikit-learn certification.