the scikit-learn sidekick

Elevate ML Development with Built-in Recommended Practices

🧩 What is Skore?

Skore is a product whose core mission is to turn uneven ML development into structured, effective decision-making. It is made of two complementary components:

• Skore Lib: the scikit-learn sidekick, an open-source Python library (described here!) designed to help data scientists boost their ML development with effective guidance and tooling.

• Skore Hub: the collaborative layer where teams connect, learn more on our product page.

Key features of Skore Lib

Evaluate and inspect: 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 model. All in just one 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 Lib 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.