User guide#
Below is a gallery of narrated notebook examples on how and why to use skore. They serve as our user guide.
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.
EstimatorReport: Get insights from any scikit-learn estimator
train_test_split: get diagnostics when splitting your data
Technical details#
These examples show some technical details at the core of skore to better understand some of the mechanics under the hood.
