the scikit-learn sidekick

Elevate ML Development with Built-in Recommended Practices

Where to start?

See our Quick start page!

What is skore?

skore is a Python open-source library designed to help data scientists apply recommended practices and avoid common methodological pitfalls in scikit-learn.

Key features

• Evaluate: automated insightful reports.

  • skore.EstimatorReport: feed your scikit-learn compatible estimator and dataset, and it generates recommended metrics, feature importance, and plots to help you evaluate and inspect your estimator. All these are computed and generated for you in 1 line of code. Under the hood, we use efficient caching to make the computations blazing fast.

  • skore.CrossValidationReport: get a skore estimator report for each fold of your cross-validation.

  • skore.ComparisonReport: benchmark your skore estimator reports.

• Diagnose: catch methodological errors before they impact your models.

  • skore.train_test_split() supercharged with methodological guidance: the API is the same as scikit-learn’s, but skore displays warnings when applicable. For example, it warns you against shuffling time series data or when you have class imbalance.

What’s next?

Skore is just at the beginning of its journey, but we’re shipping fast! Frequent updates and new features are on the way as we work toward our vision of becoming a comprehensive library for data scientists.