Examples#
Below is a gallery of narrated notebook examples on how and why to use skore.
Note
If you would like to run the examples, please install their dependencies using
pip install -U skore[sphinx]
Getting started#
We recommend starting with these examples that provide an overall and gentle introduction to skore.
End-to-end data science use cases#
These examples showcase skore in action on real use cases. We aimed at showing skore’s ability to:
be compatible with scikit-learn
reduce boilerplate code for some standard de facto data science analysis
speed-up exploration by optimizing some internal computation
EstimatorReport: Inspecting your models with the feature importance
Model evaluation#
These examples illustrate how skore can help data scientists to improve their machine learning modelling thanks to methodological guidance and diagnostics.
Adapt skore to your use-case by adding your own metrics
EstimatorReport: Get insights from any scikit-learn estimator
Checking for modeling pitfalls and remediation#
Examples for each automated check. Each script reproduces a check, and walks through fixes described in Automated checks.
Integrations#
These examples show how skore integrates with other libraries of the Python data
science ecosystem.
Using skore with scikit-learn compatible estimators
Technical details#
These examples show some technical details at the core of skore to better understand some of the mechanics under the hood.
