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  • Install
  • User guide
  • Examples
  • API
  • Contributing
  • Changelog
  • 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
    • train_test_split: get diagnostics when splitting your data
  • 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 diagnostic checks
    • Automatic detection of modelling issues
    • Cache mechanism
    • Local skore Project
    • The skore API
  • Examples
  • Technical details

Technical details#

These examples show some technical details at the core of skore to better understand some of the mechanics under the hood.

Adding custom diagnostic checks

Adding custom diagnostic checks

Automatic detection of modelling issues

Automatic detection of modelling issues

Cache mechanism

Cache mechanism

Local skore Project

Local skore Project

The skore API

The skore API

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Using skore with scikit-learn compatible estimators

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Adding custom diagnostic checks
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