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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
  • Integrations

Integrations#

These examples show how skore integrates with other libraries of the Python data science ecosystem.

Store and retrieve Skore reports in MLflow

Store and retrieve Skore reports in MLflow

Store and retrieve reports on Skore Hub

Store and retrieve reports on Skore Hub

Using skore with scikit-learn compatible estimators

Using skore with scikit-learn compatible estimators

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train_test_split: get diagnostics when splitting your data

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Store and retrieve Skore reports in MLflow

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