the scikit-learn sidekick

Elevate ML Development with Built-in Recommended Practices
Documentation — Community

Why skore?

ML development is an art — blending business sense, stats knowledge, and coding skills. Brought to you by Probabl, a company co-founded by scikit-learn core developers, skore helps data scientists focus on what matters: building impactful models with hindsight and clarity.

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, supporting every phase of the machine learning lifecycle.

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Key features

• Diagnose: Catch methodological errors before they impact your models with smart alerts that analyze both code execution and data patterns in real-time.
• Evaluate: Uncover actionable insights through automated reports surfacing relevant metrics. Explore faster with our intelligent caching system.

🚀 Quick start

Installation

With pip

We recommend using a virtual environment (venv). You need python>=3.9.

Then, you can install skore by using pip:

pip install -U skore

With conda

skore is available in conda-forge:

conda install conda-forge::skore

You can find information on the latest version here.

Get assistance when developing your ML/DS projects

1. From your Python code, create and load a skore project:

   import skore
   my_project = skore.open("my_project")

   This will create a skore project directory named my_project.skore in your current working directory.

2. Evaluate your model using skore.CrossValidationReporter:

   from sklearn.datasets import make_classification
   from sklearn.linear_model import LogisticRegression
   
   from skore import CrossValidationReport
   
   X, y = make_classification(n_classes=2, n_samples=100_000, n_informative=4)
   clf = LogisticRegression()
   
   cv_report = CrossValidationReport(clf, X, y)
   
   # Display the help tree to see all the insights that are available to you
   cv_report.help()

   # Display the report metrics that was computed for you:
   df_cv_report_metrics = cv_report.metrics.report_metrics()
   df_cv_report_metrics

   # Display the ROC curve that was generated for you:
   roc_plot = cv_report.metrics.plot.roc()
   roc_plot

3. Store the results in the skore project for safe-keeping:

   my_project.put("df_cv_report_metrics", df_cv_report_metrics)
   my_project.put("roc_plot", roc_plot)

Learn more in our documentation.

Contributing

Thank you for considering contributing to skore! Join our mission to promote open-source and make machine learning development more robust and effective. Please check the contributing guidelines here.

Feedback & Community

• Join our Discord to share ideas or get support.
• Request a feature or report a bug via GitHub Issues.

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