Skip to main content
Ctrl+K
skore - Home skore - Home
  • Install
  • User guide
  • Examples
  • API
  • Contributing
  • Probabl
  • GitHub
  • Discord
  • YouTube
  • Install
  • User guide
  • Examples
  • API
  • Contributing
  • Probabl
  • GitHub
  • Discord
  • YouTube

Section Navigation

  • Getting started
    • Skore: getting started
  • End-to-end data science use cases
    • EstimatorReport: Inspecting your models with the feature importance
    • Simplified and structured experiment reporting
    • Tracking all the data processing
  • Model evaluation
    • Adapt skore to your use-case by adding your own metrics
    • EstimatorReport: Get insights from any scikit-learn estimator
  • Integrations
    • Store and retrieve Skore reports in MLflow
    • Store and retrieve reports on Skore Hub
    • Using skore with scikit-learn compatible estimators
  • Technical details
    • Adding custom checks
    • Automatic detection of modelling issues
    • Cache mechanism
    • Local skore Project
    • The skore API
  • Examples
  • Model evaluation

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

Adapt skore to your use-case by adding your own metrics

EstimatorReport: Get insights from any scikit-learn estimator

EstimatorReport: Get insights from any scikit-learn estimator

previous

Tracking all the data processing

next

Adapt skore to your use-case by adding your own metrics

Show Source

© Copyright 2026, Probabl.

Created using Sphinx 8.1.3.

Built with the PyData Sphinx Theme 0.18.0.