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  • Install
  • User guide
  • API
  • Contributing
  • Probabl
  • GitHub
  • Discord
  • YouTube

Section Navigation

  • Getting started
    • Quick start
    • Skore: getting started
  • End-to-end data science use cases
    • EstimatorReport: Inspecting your models with the feature importance
    • Simplified experiment reporting
  • Model evaluation
    • EstimatorReport: Get insights from any scikit-learn estimator
    • train_test_split: get diagnostics when splitting your data
  • Manipulating the skore project
    • Tracking items
    • Working with projects
  • Technical details
    • Cache mechanism
  • User guide
  • 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.

EstimatorReport: Get insights from any scikit-learn estimator

EstimatorReport: Get insights from any scikit-learn estimator

train_test_split: get diagnostics when splitting your data

train_test_split: get diagnostics when splitting your data

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Simplified experiment reporting

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EstimatorReport: Get insights from any scikit-learn estimator

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