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  • 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
  • Checking for modeling pitfalls and remediation
    • SKD001 & SKD002 - Overfitting and underfitting
    • SKD004 - High class imbalance
    • SKD014 & SKD015: Hyperparameter search pitfalls
  • 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
    • Using skrub DataOp cross-validation
  • Examples
  • Checking for modeling pitfalls and remediation

Checking for modeling pitfalls and remediation#

Examples for each automated check. Each script reproduces a check, and walks through fixes described in Automated checks.

SKD001 & SKD002 - Overfitting and underfitting

SKD001 & SKD002 - Overfitting and underfitting

SKD004 - High class imbalance

SKD004 - High class imbalance

SKD014 & SKD015: Hyperparameter search pitfalls

SKD014 & SKD015: Hyperparameter search pitfalls

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EstimatorReport: Get insights from any scikit-learn estimator

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SKD001 & SKD002 - Overfitting and underfitting

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