{"id":24055784,"url":"https://github.com/probabl-ai/skore","last_synced_at":"2026-04-20T17:05:41.598Z","repository":{"id":257807294,"uuid":"816360433","full_name":"probabl-ai/skore","owner":"probabl-ai","description":"the scikit-learn sidekick","archived":false,"fork":false,"pushed_at":"2025-05-12T09:39:33.000Z","size":11031,"stargazers_count":436,"open_issues_count":122,"forks_count":69,"subscribers_count":7,"default_branch":"main","last_synced_at":"2025-05-12T10:03:39.334Z","etag":null,"topics":["data-analysis","data-science","data-visualization","machine-learning","python","scikit-learn","workflow"],"latest_commit_sha":null,"homepage":"https://docs.skore.probabl.ai","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/probabl-ai.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.rst","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":".github/CODEOWNERS","security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2024-06-17T15:29:38.000Z","updated_at":"2025-05-12T09:39:34.000Z","dependencies_parsed_at":null,"dependency_job_id":"34183396-4a83-4f7f-913d-8ea2b392ca17","html_url":"https://github.com/probabl-ai/skore","commit_stats":null,"previous_names":["probabl-ai/mandr"],"tags_count":24,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/probabl-ai%2Fskore","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/probabl-ai%2Fskore/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/probabl-ai%2Fskore/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/probabl-ai%2Fskore/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/probabl-ai","download_url":"https://codeload.github.com/probabl-ai/skore/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254270640,"owners_count":22042858,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["data-analysis","data-science","data-visualization","machine-learning","python","scikit-learn","workflow"],"created_at":"2025-01-09T04:30:57.502Z","updated_at":"2026-04-20T17:05:41.591Z","avatar_url":"https://github.com/probabl-ai.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\u003cdiv align=\"center\"\u003e\n\n  ![license](https://img.shields.io/pypi/l/skore)\n  ![python](https://img.shields.io/badge/python-3.10%20%7C%203.11%20%7C%203.12%20%7C%203.13-blue?style=flat\u0026logo=python)\n  [![downloads](https://static.pepy.tech/badge/skore/month)](https://pepy.tech/projects/skore)\n  [![pypi](https://img.shields.io/pypi/v/skore)](https://pypi.org/project/skore/)\n  [![Discord](https://img.shields.io/discord/1275821367324840119?label=Discord)](https://discord.probabl.ai/)\n\n\u003c/div\u003e\n\n\u003cdiv align=\"center\"\u003e\n\n  \u003cpicture\u003e\n    \u003csource srcset=\"https://media.githubusercontent.com/media/probabl-ai/skore/main/sphinx/_static/images/Logo_Skore_Dark@2x.svg\" media=\"(prefers-color-scheme: dark)\"\u003e\n    \u003cimg width=\"200\" src=\"https://media.githubusercontent.com/media/probabl-ai/skore/main/sphinx/_static/images/Logo_Skore_Light@2x.svg\" alt=\"skore logo\"\u003e\n  \u003c/picture\u003e\n  \u003ch3\u003eTrack Your Data Science\u003c/h3\u003e\n\nElevate ML Development with Built-in Recommended Practices \\\n[Documentation](https://docs.skore.probabl.ai) — [Community](https://discord.probabl.ai) — [YouTube](https://youtube.com/playlist?list=PLSIzlWDI17bTpixfFkooxLpbz4DNQcam3) — [Skore Hub](https://probabl.ai/skore)\n\n\u003c/div\u003e\n\n\u003cbr /\u003e\n\n## 🎯 Why Skore?\n\nWhen it comes to data science, you have excellent tools at your disposal: `pandas` and `polars` for data exploration, `skrub` for stateful transformations, and `scikit-learn` for model training and evaluation. These libraries are designed to be generic and accommodate a wide range of use cases.\n\n**But here's the challenge**: Your experience is key to choosing the right building blocks and methodologies. You often spend significant time navigating documentation, writing boilerplate code for common evaluations, and struggling to maintain clear project structure.\n\n**Skore is the conductor** that transforms your data science pipeline into structured, meaningful artifacts. It reduces the time you spend on documentation navigation, eliminates boilerplate code, and guides you toward the right methodological information to answer your questions.\n\n### What Skore does for you:\n\n- **Structures your experiments**: Automatically generates the insights that matter for your use case\n- **Reduces boilerplate**: One line of code gives you comprehensive model evaluation\n- **Guides your decisions**: Built-in methodological warnings help you avoid common pitfalls\n- **Maintains clarity**: Structured project organization makes your work easier to understand and maintain\n\n⭐ Support us with a star and spread the word - it means a lot! ⭐\n\n## 🧩 What is Skore?\n\nThe core mission of **Skore** is to turn uneven ML development into structured, effective decision-making. It consists of two complementary components:\n- **Skore Lib**: the open-source Python library (described here!) that provides the structured artifacts and methodological guidance for your data science experiments.\n- **Skore Hub**: the collaborative platform where teams can share, compare, and build upon each other's structured experiments. Learn more on our [product page](https://probabl.ai/skore).\n\n## ⚡️ Quick start\n\n### Installation\n\n#### With pip\n\nWe recommend using a [virtual environment (venv)](https://docs.python.org/3/tutorial/venv.html). You need `python\u003e=3.10`.\n\nThen, you can install skore by using `pip`:\n```bash\n# If you plan to use Skore locally\npip install -U skore\n# If you wish to interact with Skore Hub as well\npip install -U skore[hub]\n# If you wish to log projects to MLflow\npip install -U skore[mlflow]\n```\n\n#### With conda\n\nskore is available in `conda-forge` both for local and hub use:\n\n```bash\nconda install conda-forge::skore\n```\n\nYou can find information on the latest version [here](https://anaconda.org/conda-forge/skore).\n\n### Get structured insights from your ML pipeline\n\nEvaluate your model and get comprehensive insights in one line:\n\n```python\nfrom sklearn.datasets import make_classification\nfrom sklearn.linear_model import LogisticRegression\nfrom skore import CrossValidationReport\n\nX, y = make_classification(n_classes=2, n_samples=100_000, n_informative=4)\nclf = LogisticRegression()\n\n# Get structured insights that matter for your use case\ncv_report = CrossValidationReport(clf, X, y)\n\n# See what insights are available\ncv_report.help()\n\n# Example: Access the metrics summary\nmetrics_summary = cv_report.metrics.summarize().frame()\n\n# Example: Get the ROC curve\nroc_plot = cv_report.metrics.roc()\nroc_plot.plot()\n```\n\nLearn more in our [documentation](https://docs.skore.probabl.ai).\n\n## 🛠️ Contributing\n\nJoin our mission to promote open-source and make machine learning development more robust and effective. If you'd like to contribute, please check the contributing guidelines [here](https://github.com/probabl-ai/skore/blob/main/CONTRIBUTING.rst).\n\n## 👋 Feedback \u0026 Community\n\n-   Join our [Discord](https://discord.probabl.ai/) to share ideas or get support.\n-   Request a feature or report a bug via [GitHub Issues](https://github.com/probabl-ai/skore/issues).\n\n## Support\n\nSkore is tested on Linux and Windows, for at most 4 versions of Python, and at most 4 versions of scikit-learn:\n- Python 3.11\n  - scikit-learn 1.5\n  - scikit-learn 1.8\n- Python 3.12\n  - scikit-learn 1.5\n  - scikit-learn 1.8\n- Python 3.13\n  - scikit-learn 1.5\n  - scikit-learn 1.8\n- Python 3.14\n  - scikit-learn 1.7\n  - scikit-learn 1.8\n\n---\n\nBrought to you by\n\n\u003ca href=\"https://probabl.ai/skore\" target=\"_blank\"\u003e\n    \u003cpicture\u003e\n        \u003csource srcset=\"https://media.githubusercontent.com/media/probabl-ai/skore/main/sphinx/_static/images/Probabl-logo-orange.png\" media=\"(prefers-color-scheme: dark)\"\u003e\n        \u003cimg width=\"120\" src=\"https://media.githubusercontent.com/media/probabl-ai/skore/main/sphinx/_static/images/Probabl-logo-blue.png\" alt=\"Probabl logo\"\u003e\n    \u003c/picture\u003e\n\u003c/a\u003e\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprobabl-ai%2Fskore","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fprobabl-ai%2Fskore","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fprobabl-ai%2Fskore/lists"}