[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"similar-scikit-learn--scikit-learn":3,"tool-scikit-learn--scikit-learn":64},[4,17,27,35,43,56],{"id":5,"name":6,"github_repo":7,"description_zh":8,"stars":9,"difficulty_score":10,"last_commit_at":11,"category_tags":12,"status":16},3808,"stable-diffusion-webui","AUTOMATIC1111\u002Fstable-diffusion-webui","stable-diffusion-webui 是一个基于 Gradio 构建的网页版操作界面，旨在让用户能够轻松地在本地运行和使用强大的 Stable Diffusion 图像生成模型。它解决了原始模型依赖命令行、操作门槛高且功能分散的痛点，将复杂的 AI 绘图流程整合进一个直观易用的图形化平台。\n\n无论是希望快速上手的普通创作者、需要精细控制画面细节的设计师，还是想要深入探索模型潜力的开发者与研究人员，都能从中获益。其核心亮点在于极高的功能丰富度：不仅支持文生图、图生图、局部重绘（Inpainting）和外绘（Outpainting）等基础模式，还独创了注意力机制调整、提示词矩阵、负向提示词以及“高清修复”等高级功能。此外，它内置了 GFPGAN 和 CodeFormer 等人脸修复工具，支持多种神经网络放大算法，并允许用户通过插件系统无限扩展能力。即使是显存有限的设备，stable-diffusion-webui 也提供了相应的优化选项，让高质量的 AI 艺术创作变得触手可及。",162132,3,"2026-04-05T11:01:52",[13,14,15],"开发框架","图像","Agent","ready",{"id":18,"name":19,"github_repo":20,"description_zh":21,"stars":22,"difficulty_score":23,"last_commit_at":24,"category_tags":25,"status":16},1381,"everything-claude-code","affaan-m\u002Feverything-claude-code","everything-claude-code 是一套专为 AI 编程助手（如 Claude Code、Codex、Cursor 等）打造的高性能优化系统。它不仅仅是一组配置文件，而是一个经过长期实战打磨的完整框架，旨在解决 AI 代理在实际开发中面临的效率低下、记忆丢失、安全隐患及缺乏持续学习能力等核心痛点。\n\n通过引入技能模块化、直觉增强、记忆持久化机制以及内置的安全扫描功能，everything-claude-code 能显著提升 AI 在复杂任务中的表现，帮助开发者构建更稳定、更智能的生产级 AI 代理。其独特的“研究优先”开发理念和针对 Token 消耗的优化策略，使得模型响应更快、成本更低，同时有效防御潜在的攻击向量。\n\n这套工具特别适合软件开发者、AI 研究人员以及希望深度定制 AI 工作流的技术团队使用。无论您是在构建大型代码库，还是需要 AI 协助进行安全审计与自动化测试，everything-claude-code 都能提供强大的底层支持。作为一个曾荣获 Anthropic 黑客大奖的开源项目，它融合了多语言支持与丰富的实战钩子（hooks），让 AI 真正成长为懂上",138956,2,"2026-04-05T11:33:21",[13,15,26],"语言模型",{"id":28,"name":29,"github_repo":30,"description_zh":31,"stars":32,"difficulty_score":23,"last_commit_at":33,"category_tags":34,"status":16},2271,"ComfyUI","Comfy-Org\u002FComfyUI","ComfyUI 是一款功能强大且高度模块化的视觉 AI 引擎，专为设计和执行复杂的 Stable Diffusion 图像生成流程而打造。它摒弃了传统的代码编写模式，采用直观的节点式流程图界面，让用户通过连接不同的功能模块即可构建个性化的生成管线。\n\n这一设计巧妙解决了高级 AI 绘图工作流配置复杂、灵活性不足的痛点。用户无需具备编程背景，也能自由组合模型、调整参数并实时预览效果，轻松实现从基础文生图到多步骤高清修复等各类复杂任务。ComfyUI 拥有极佳的兼容性，不仅支持 Windows、macOS 和 Linux 全平台，还广泛适配 NVIDIA、AMD、Intel 及苹果 Silicon 等多种硬件架构，并率先支持 SDXL、Flux、SD3 等前沿模型。\n\n无论是希望深入探索算法潜力的研究人员和开发者，还是追求极致创作自由度的设计师与资深 AI 绘画爱好者，ComfyUI 都能提供强大的支持。其独特的模块化架构允许社区不断扩展新功能，使其成为当前最灵活、生态最丰富的开源扩散模型工具之一，帮助用户将创意高效转化为现实。",107662,"2026-04-03T11:11:01",[13,14,15],{"id":36,"name":37,"github_repo":38,"description_zh":39,"stars":40,"difficulty_score":23,"last_commit_at":41,"category_tags":42,"status":16},3704,"NextChat","ChatGPTNextWeb\u002FNextChat","NextChat 是一款轻量且极速的 AI 助手，旨在为用户提供流畅、跨平台的大模型交互体验。它完美解决了用户在多设备间切换时难以保持对话连续性，以及面对众多 AI 模型不知如何统一管理的痛点。无论是日常办公、学习辅助还是创意激发，NextChat 都能让用户随时随地通过网页、iOS、Android、Windows、MacOS 或 Linux 端无缝接入智能服务。\n\n这款工具非常适合普通用户、学生、职场人士以及需要私有化部署的企业团队使用。对于开发者而言，它也提供了便捷的自托管方案，支持一键部署到 Vercel 或 Zeabur 等平台。\n\nNextChat 的核心亮点在于其广泛的模型兼容性，原生支持 Claude、DeepSeek、GPT-4 及 Gemini Pro 等主流大模型，让用户在一个界面即可自由切换不同 AI 能力。此外，它还率先支持 MCP（Model Context Protocol）协议，增强了上下文处理能力。针对企业用户，NextChat 提供专业版解决方案，具备品牌定制、细粒度权限控制、内部知识库整合及安全审计等功能，满足公司对数据隐私和个性化管理的高标准要求。",87618,"2026-04-05T07:20:52",[13,26],{"id":44,"name":45,"github_repo":46,"description_zh":47,"stars":48,"difficulty_score":23,"last_commit_at":49,"category_tags":50,"status":16},2268,"ML-For-Beginners","microsoft\u002FML-For-Beginners","ML-For-Beginners 是由微软推出的一套系统化机器学习入门课程，旨在帮助零基础用户轻松掌握经典机器学习知识。这套课程将学习路径规划为 12 周，包含 26 节精炼课程和 52 道配套测验，内容涵盖从基础概念到实际应用的完整流程，有效解决了初学者面对庞大知识体系时无从下手、缺乏结构化指导的痛点。\n\n无论是希望转型的开发者、需要补充算法背景的研究人员，还是对人工智能充满好奇的普通爱好者，都能从中受益。课程不仅提供了清晰的理论讲解，还强调动手实践，让用户在循序渐进中建立扎实的技能基础。其独特的亮点在于强大的多语言支持，通过自动化机制提供了包括简体中文在内的 50 多种语言版本，极大地降低了全球不同背景用户的学习门槛。此外，项目采用开源协作模式，社区活跃且内容持续更新，确保学习者能获取前沿且准确的技术资讯。如果你正寻找一条清晰、友好且专业的机器学习入门之路，ML-For-Beginners 将是理想的起点。",84991,"2026-04-05T10:45:23",[14,51,52,53,15,54,26,13,55],"数据工具","视频","插件","其他","音频",{"id":57,"name":58,"github_repo":59,"description_zh":60,"stars":61,"difficulty_score":10,"last_commit_at":62,"category_tags":63,"status":16},3128,"ragflow","infiniflow\u002Fragflow","RAGFlow 是一款领先的开源检索增强生成（RAG）引擎，旨在为大语言模型构建更精准、可靠的上下文层。它巧妙地将前沿的 RAG 技术与智能体（Agent）能力相结合，不仅支持从各类文档中高效提取知识，还能让模型基于这些知识进行逻辑推理和任务执行。\n\n在大模型应用中，幻觉问题和知识滞后是常见痛点。RAGFlow 通过深度解析复杂文档结构（如表格、图表及混合排版），显著提升了信息检索的准确度，从而有效减少模型“胡编乱造”的现象，确保回答既有据可依又具备时效性。其内置的智能体机制更进一步，使系统不仅能回答问题，还能自主规划步骤解决复杂问题。\n\n这款工具特别适合开发者、企业技术团队以及 AI 研究人员使用。无论是希望快速搭建私有知识库问答系统，还是致力于探索大模型在垂直领域落地的创新者，都能从中受益。RAGFlow 提供了可视化的工作流编排界面和灵活的 API 接口，既降低了非算法背景用户的上手门槛，也满足了专业开发者对系统深度定制的需求。作为基于 Apache 2.0 协议开源的项目，它正成为连接通用大模型与行业专有知识之间的重要桥梁。",77062,"2026-04-04T04:44:48",[15,14,13,26,54],{"id":65,"github_repo":66,"name":67,"description_en":68,"description_zh":69,"ai_summary_zh":70,"readme_en":71,"readme_zh":72,"quickstart_zh":73,"use_case_zh":74,"hero_image_url":75,"owner_login":67,"owner_name":67,"owner_avatar_url":76,"owner_bio":77,"owner_company":78,"owner_location":78,"owner_email":78,"owner_twitter":78,"owner_website":79,"owner_url":80,"languages":81,"stars":116,"forks":117,"last_commit_at":118,"license":119,"difficulty_score":93,"env_os":120,"env_gpu":121,"env_ram":120,"env_deps":122,"category_tags":130,"github_topics":131,"view_count":137,"oss_zip_url":78,"oss_zip_packed_at":78,"status":16,"created_at":138,"updated_at":139,"faqs":140,"releases":169},2234,"scikit-learn\u002Fscikit-learn","scikit-learn","scikit-learn: machine learning in Python","scikit-learn 是一个基于 Python 构建的开源机器学习库，依托于 SciPy、NumPy 等科学计算生态，旨在让机器学习变得简单高效。它提供了一套统一且简洁的接口，涵盖了从数据预处理、特征工程到模型训练、评估及选择的全流程工具，内置了包括线性回归、支持向量机、随机森林、聚类等在内的丰富经典算法。\n\n对于希望快速验证想法或构建原型的数据科学家、研究人员以及 Python 开发者而言，scikit-learn 是不可或缺的基础设施。它有效解决了机器学习入门门槛高、算法实现复杂以及不同模型间调用方式不统一的痛点，让用户无需重复造轮子，只需几行代码即可调用成熟的算法解决分类、回归、聚类等实际问题。\n\n其核心技术亮点在于高度一致的 API 设计风格，所有估算器（Estimator）均遵循相同的调用逻辑，极大地降低了学习成本并提升了代码的可读性与可维护性。此外，它还提供了强大的模型选择与评估工具，如交叉验证和网格搜索，帮助用户系统地优化模型性能。作为一个由全球志愿者共同维护的成熟项目，scikit-learn 以其稳定性、详尽的文档和活跃的社区支持，成为连接理论学习与工业级应用的最","scikit-learn 是一个基于 Python 构建的开源机器学习库，依托于 SciPy、NumPy 等科学计算生态，旨在让机器学习变得简单高效。它提供了一套统一且简洁的接口，涵盖了从数据预处理、特征工程到模型训练、评估及选择的全流程工具，内置了包括线性回归、支持向量机、随机森林、聚类等在内的丰富经典算法。\n\n对于希望快速验证想法或构建原型的数据科学家、研究人员以及 Python 开发者而言，scikit-learn 是不可或缺的基础设施。它有效解决了机器学习入门门槛高、算法实现复杂以及不同模型间调用方式不统一的痛点，让用户无需重复造轮子，只需几行代码即可调用成熟的算法解决分类、回归、聚类等实际问题。\n\n其核心技术亮点在于高度一致的 API 设计风格，所有估算器（Estimator）均遵循相同的调用逻辑，极大地降低了学习成本并提升了代码的可读性与可维护性。此外，它还提供了强大的模型选择与评估工具，如交叉验证和网格搜索，帮助用户系统地优化模型性能。作为一个由全球志愿者共同维护的成熟项目，scikit-learn 以其稳定性、详尽的文档和活跃的社区支持，成为连接理论学习与工业级应用的最佳桥梁。",".. -*- mode: rst -*-\n\n|GitHubActions| |Codecov| |CircleCI| |Nightly wheels| |Ruff| |PythonVersion| |PyPI| |DOI| |Benchmark|\n\n\n.. |GitHubActions| image:: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Factions\u002Fworkflows\u002Funit-tests.yml\u002Fbadge.svg\n   :target: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Factions\u002Fworkflows\u002Funit-tests.yml?query=branch%3Amain\n\n.. |CircleCI| image:: https:\u002F\u002Fcircleci.com\u002Fgh\u002Fscikit-learn\u002Fscikit-learn\u002Ftree\u002Fmain.svg?style=shield\n   :target: https:\u002F\u002Fcircleci.com\u002Fgh\u002Fscikit-learn\u002Fscikit-learn\n\n.. |Codecov| image:: https:\u002F\u002Fcodecov.io\u002Fgh\u002Fscikit-learn\u002Fscikit-learn\u002Fbranch\u002Fmain\u002Fgraph\u002Fbadge.svg?token=Pk8G9gg3y9\n   :target: https:\u002F\u002Fcodecov.io\u002Fgh\u002Fscikit-learn\u002Fscikit-learn\n\n.. |Nightly wheels| image:: 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https:\u002F\u002Fzenodo.org\u002Fbadge\u002Flatestdoi\u002F21369\u002Fscikit-learn\u002Fscikit-learn\n\n.. |Benchmark| image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBenchmarked%20by-asv-blue\n   :target: https:\u002F\u002Fscikit-learn.org\u002Fscikit-learn-benchmarks\n\n.. |PythonMinVersion| replace:: 3.11\n.. |NumPyMinVersion| replace:: 1.24.1\n.. |SciPyMinVersion| replace:: 1.10.0\n.. |JoblibMinVersion| replace:: 1.3.0\n.. |ThreadpoolctlMinVersion| replace:: 3.2.0\n.. |MatplotlibMinVersion| replace:: 3.6.1\n.. |Scikit-ImageMinVersion| replace:: 0.22.0\n.. |PandasMinVersion| replace:: 1.5.0\n.. |SeabornMinVersion| replace:: 0.13.0\n.. |PytestMinVersion| replace:: 7.1.2\n.. |PlotlyMinVersion| replace:: 5.18.0\n\n.. image:: https:\u002F\u002Fraw.githubusercontent.com\u002Fscikit-learn\u002Fscikit-learn\u002Fmain\u002Fdoc\u002Flogos\u002Fscikit-learn-logo.png\n  :target: https:\u002F\u002Fscikit-learn.org\u002F\n\n**scikit-learn** is a Python module for machine learning built on top of\nSciPy and is distributed under the 3-Clause BSD license.\n\nThe project was started in 2007 by David Cournapeau as a Google Summer\nof Code project, and since then many volunteers have contributed. See\nthe `About us \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fabout.html#authors>`__ page\nfor a list of core contributors.\n\nIt is currently maintained by a team of volunteers.\n\nWebsite: https:\u002F\u002Fscikit-learn.org\n\nInstallation\n------------\n\nDependencies\n~~~~~~~~~~~~\n\nscikit-learn requires:\n\n- Python (>= |PythonMinVersion|)\n- NumPy (>= |NumPyMinVersion|)\n- SciPy (>= |SciPyMinVersion|)\n- joblib (>= |JoblibMinVersion|)\n- threadpoolctl (>= |ThreadpoolctlMinVersion|)\n\n=======\n\nScikit-learn plotting capabilities (i.e., functions start with ``plot_`` and\nclasses end with ``Display``) require Matplotlib (>= |MatplotlibMinVersion|).\nFor running the examples Matplotlib >= |MatplotlibMinVersion| is required.\nA few examples require scikit-image >= |Scikit-ImageMinVersion|, a few examples\nrequire pandas >= |PandasMinVersion|, some examples require seaborn >=\n|SeabornMinVersion| and Plotly >= |PlotlyMinVersion|.\n\nUser installation\n~~~~~~~~~~~~~~~~~\n\nIf you already have a working installation of NumPy and SciPy,\nthe easiest way to install scikit-learn is using ``pip``::\n\n    pip install -U scikit-learn\n\nor ``conda``::\n\n    conda install -c conda-forge scikit-learn\n\nThe documentation includes more detailed `installation instructions \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fstable\u002Finstall.html>`_.\n\n\nChangelog\n---------\n\nSee the `changelog \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fwhats_new.html>`__\nfor a history of notable changes to scikit-learn.\n\nDevelopment\n-----------\n\nWe welcome new contributors of all experience levels. The scikit-learn\ncommunity goals are to be helpful, welcoming, and effective. The\n`Development Guide \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fdevelopers\u002Findex.html>`_\nhas detailed information about contributing code, documentation, tests, and\nmore. We've included some basic information in this README.\n\nImportant links\n~~~~~~~~~~~~~~~\n\n- Official source code repo: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\n- Download releases: https:\u002F\u002Fpypi.org\u002Fproject\u002Fscikit-learn\u002F\n- Issue tracker: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fissues\n\nSource code\n~~~~~~~~~~~\n\nYou can check the latest sources with the command::\n\n    git clone https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn.git\n\nContributing\n~~~~~~~~~~~~\n\nTo learn more about making a contribution to scikit-learn, please see our\n`Contributing guide\n\u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fdevelopers\u002Fcontributing.html>`_.\n\nTesting\n~~~~~~~\n\nAfter installation, you can launch the test suite from outside the source\ndirectory (you will need to have ``pytest`` >= |PytestMinVersion| installed)::\n\n    pytest sklearn\n\nSee the web page https:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fdevelopers\u002Fcontributing.html#testing-and-improving-test-coverage\nfor more information.\n\n    Random number generation can be controlled during testing by setting\n    the ``SKLEARN_SEED`` environment variable.\n\nSubmitting a Pull Request\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\nBefore opening a Pull Request, have a look at the\nfull Contributing page to make sure your code complies\nwith our guidelines: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fdevelopers\u002Findex.html\n\nProject History\n---------------\n\nThe project was started in 2007 by David Cournapeau as a Google Summer\nof Code project, and since then many volunteers have contributed. See\nthe `About us \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fabout.html#authors>`__ page\nfor a list of core contributors.\n\nThe project is currently maintained by a team of volunteers.\n\n**Note**: `scikit-learn` was previously referred to as `scikits.learn`.\n\nHelp and Support\n----------------\n\nDocumentation\n~~~~~~~~~~~~~\n\n- HTML documentation (stable release): https:\u002F\u002Fscikit-learn.org\n- HTML documentation (development version): https:\u002F\u002Fscikit-learn.org\u002Fdev\u002F\n- FAQ: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Ffaq.html\n\nCommunication\n~~~~~~~~~~~~~\n\nMain Channels\n^^^^^^^^^^^^^\n\n- **Website**: https:\u002F\u002Fscikit-learn.org\n- **Blog**: https:\u002F\u002Fblog.scikit-learn.org\n- **Mailing list**: https:\u002F\u002Fmail.python.org\u002Fmailman\u002Flistinfo\u002Fscikit-learn\n\nDeveloper & Support\n^^^^^^^^^^^^^^^^^^^^^^\n\n- **GitHub Discussions**: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fdiscussions\n- **Stack Overflow**: https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fscikit-learn\n- **Discord**: https:\u002F\u002Fdiscord.gg\u002Fh9qyrK8Jc8\n\nSocial Media Platforms\n^^^^^^^^^^^^^^^^^^^^^^\n\n- **LinkedIn**: https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fscikit-learn\n- **YouTube**: https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCJosFjYm0ZYVUARxuOZqnnw\u002Fplaylists\n- **Facebook**: https:\u002F\u002Fwww.facebook.com\u002Fscikitlearnofficial\u002F\n- **Instagram**: https:\u002F\u002Fwww.instagram.com\u002Fscikitlearnofficial\u002F\n- **TikTok**: https:\u002F\u002Fwww.tiktok.com\u002F@scikit.learn\n- **Bluesky**: https:\u002F\u002Fbsky.app\u002Fprofile\u002Fscikit-learn.org\n- **Mastodon**: https:\u002F\u002Fmastodon.social\u002F@sklearn@fosstodon.org\n\nResources\n^^^^^^^^^\n\n- **Calendar**: https:\u002F\u002Fblog.scikit-learn.org\u002Fcalendar\u002F\n- **Logos & Branding**: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Ftree\u002Fmain\u002Fdoc\u002Flogos\n\nCitation\n~~~~~~~~\n\nIf you use scikit-learn in a scientific publication, we would appreciate citations: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fabout.html#citing-scikit-learn\n",".. -*- mode: rst -*-\n\n|GitHubActions| |Codecov| |CircleCI| |Nightly wheels| |Ruff| |PythonVersion| |PyPI| |DOI| |Benchmark|\n\n\n.. |GitHubActions| image:: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Factions\u002Fworkflows\u002Funit-tests.yml\u002Fbadge.svg\n   :target: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Factions\u002Fworkflows\u002Funit-tests.yml?query=branch%3Amain\n\n.. |CircleCI| image:: https:\u002F\u002Fcircleci.com\u002Fgh\u002Fscikit-learn\u002Fscikit-learn\u002Ftree\u002Fmain.svg?style=shield\n   :target: https:\u002F\u002Fcircleci.com\u002Fgh\u002Fscikit-learn\u002Fscikit-learn\n\n.. |Codecov| image:: 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https:\u002F\u002Fpypi.org\u002Fproject\u002Fscikit-learn\n\n.. |DOI| image:: https:\u002F\u002Fzenodo.org\u002Fbadge\u002F21369\u002Fscikit-learn\u002Fscikit-learn.svg\n   :target: https:\u002F\u002Fzenodo.org\u002Fbadge\u002Flatestdoi\u002F21369\u002Fscikit-learn\u002Fscikit-learn\n\n.. |Benchmark| image:: https:\u002F\u002Fimg.shields.io\u002Fbadge\u002FBenchmarked%20by-asv-blue\n   :target: https:\u002F\u002Fscikit-learn.org\u002Fscikit-learn-benchmarks\n\n.. |PythonMinVersion| replace:: 3.11\n.. |NumPyMinVersion| replace:: 1.24.1\n.. |SciPyMinVersion| replace:: 1.10.0\n.. |JoblibMinVersion| replace:: 1.3.0\n.. |ThreadpoolctlMinVersion| replace:: 3.2.0\n.. |MatplotlibMinVersion| replace:: 3.6.1\n.. |Scikit-ImageMinVersion| replace:: 0.22.0\n.. |PandasMinVersion| replace:: 1.5.0\n.. |SeabornMinVersion| replace:: 0.13.0\n.. |PytestMinVersion| replace:: 7.1.2\n.. |PlotlyMinVersion| replace:: 5.18.0\n\n.. image:: https:\u002F\u002Fraw.githubusercontent.com\u002Fscikit-learn\u002Fscikit-learn\u002Fmain\u002Fdoc\u002Flogos\u002Fscikit-learn-logo.png\n  :target: https:\u002F\u002Fscikit-learn.org\u002F\n\n**scikit-learn** 是一个基于 SciPy 构建的 Python 机器学习模块，采用 3 条款 BSD 许可证进行分发。\n\n该项目于 2007 年由 David Cournapeau 作为 Google Summer of Code 项目启动，此后吸引了众多志愿者参与贡献。核心贡献者名单请参见 `关于我们 \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fabout.html#authors>`__ 页面。\n\n目前，scikit-learn 由一群志愿者维护。\n\n官网：https:\u002F\u002Fscikit-learn.org\n\n安装\n----\n\n依赖项\n~~~~~~~\n\nscikit-learn 需要以下依赖：\n\n- Python (>= |PythonMinVersion|)\n- NumPy (>= |NumPyMinVersion|)\n- SciPy (>= |SciPyMinVersion|)\n- joblib (>= |JoblibMinVersion|)\n- threadpoolctl (>= |ThreadpoolctlMinVersion|)\n\n=======\n\nscikit-learn 的绘图功能（即以 ``plot_`` 开头的函数和以 ``Display`` 结尾的类）需要 Matplotlib (>= |MatplotlibMinVersion|)。运行示例时，同样需要 Matplotlib >= |MatplotlibMinVersion|。部分示例还要求 scikit-image >= |Scikit-ImageMinVersion|、pandas >= |PandasMinVersion|、seaborn >= |SeabornMinVersion| 以及 Plotly >= |PlotlyMinVersion|。\n\n用户安装\n~~~~~~~~~\n\n如果您已经安装了可用的 NumPy 和 SciPy，最简单的安装方式是使用 ``pip``::\n\n    pip install -U scikit-learn\n\n或者使用 ``conda``::\n\n    conda install -c conda-forge scikit-learn\n\n更多详细的安装说明，请参阅文档：https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Finstall.html\n\n\n变更日志\n---------\n\n有关 scikit-learn 的重要变更历史，请参阅 `变更日志 \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fwhats_new.html>`__。\n\n开发\n----\n\n我们欢迎所有经验水平的新贡献者。scikit-learn 社区的目标是提供帮助、友好且高效的工作环境。`开发指南 \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fdevelopers\u002Findex.html>`_ 提供了关于代码、文档、测试等方面贡献的详细信息。本 README 中也包含了一些基本信息。\n\n重要链接\n~~~~~~~~~~\n\n- 官方源码仓库：https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\n- 发布版本下载：https:\u002F\u002Fpypi.org\u002Fproject\u002Fscikit-learn\u002F\n- 问题追踪器：https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fissues\n\n源码\n~~~~\n\n您可以通过以下命令获取最新源码：\n\n    git clone https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn.git\n\n贡献\n~~~~\n\n如需了解更多关于如何为 scikit-learn 做出贡献的信息，请参阅我们的 `贡献指南 \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fdevelopers\u002Fcontributing.html>`_。\n\n测试\n~~~~\n\n安装完成后，您可以在源码目录外运行测试套件（需安装 pytest >= |PytestMinVersion|）：\n\n    pytest sklearn\n\n更多信息请访问：https:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fdevelopers\u002Fcontributing.html#testing-and-improving-test-coverage\n\n在测试过程中，可以通过设置环境变量 ``SKLEARN_SEED`` 来控制随机数生成。\n\n提交 Pull Request\n~~~~~~~~~~~~~~~~~\n\n在打开 Pull Request 之前，请务必查看完整的贡献页面，确保您的代码符合我们的指南：https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fdevelopers\u002Findex.html\n\n项目历史\n--------\n\n该项目于 2007 年由 David Cournapeau 作为 Google Summer of Code 项目启动，此后吸引了众多志愿者参与贡献。核心贡献者名单请参见 `关于我们 \u003Chttps:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fabout.html#authors>`__ 页面。\n\n目前，该项目由一组志愿者维护。\n\n**注意**：`scikit-learn` 曾被称为 `scikits.learn`。\n\n帮助与支持\n------------\n\n文档\n~~~~\n\n- HTML 文档（稳定版）：https:\u002F\u002Fscikit-learn.org\n- HTML 文档（开发版）：https:\u002F\u002Fscikit-learn.org\u002Fdev\u002F\n- 常见问题解答：https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Ffaq.html\n\n沟通\n~~~~\n\n主要渠道\n^^^^^^^^\n\n- **官网**：https:\u002F\u002Fscikit-learn.org\n- **博客**：https:\u002F\u002Fblog.scikit-learn.org\n- **邮件列表**：https:\u002F\u002Fmail.python.org\u002Fmailman\u002Flistinfo\u002Fscikit-learn\n\n开发者与支持\n^^^^^^^^^^^^^^^\n\n- **GitHub Discussions**：https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fdiscussions\n- **Stack Overflow**：https:\u002F\u002Fstackoverflow.com\u002Fquestions\u002Ftagged\u002Fscikit-learn\n- **Discord**：https:\u002F\u002Fdiscord.gg\u002Fh9qyrK8Jc8\n\n社交媒体平台\n^^^^^^^^^^^^^^\n\n- **领英**: https:\u002F\u002Fwww.linkedin.com\u002Fcompany\u002Fscikit-learn\n- **YouTube**: https:\u002F\u002Fwww.youtube.com\u002Fchannel\u002FUCJosFjYm0ZYVUARxuOZqnnw\u002Fplaylists\n- **脸书**: https:\u002F\u002Fwww.facebook.com\u002Fscikitlearnofficial\u002F\n- **Instagram**: https:\u002F\u002Fwww.instagram.com\u002Fscikitlearnofficial\u002F\n- **TikTok**: https:\u002F\u002Fwww.tiktok.com\u002F@scikit.learn\n- **Bluesky**: https:\u002F\u002Fbsky.app\u002Fprofile\u002Fscikit-learn.org\n- **Mastodon**: https:\u002F\u002Fmastodon.social\u002F@sklearn@fosstodon.org\n\n资源\n^^^^^^^^^\n\n- **日历**: https:\u002F\u002Fblog.scikit-learn.org\u002Fcalendar\u002F\n- **徽标与品牌资料**: https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Ftree\u002Fmain\u002Fdoc\u002Flogos\n\n引用\n~~~~~~~~\n\n如果您在科学出版物中使用了 scikit-learn，我们非常感谢您进行引用：https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fabout.html#citing-scikit-learn","# scikit-learn 快速上手指南\n\nscikit-learn 是一个基于 SciPy 构建的 Python 机器学习模块，采用 3-Clause BSD 许可证分发。它提供了简单高效的工具用于数据挖掘和数据分析，适合各类经验水平的开发者。\n\n## 环境准备\n\n在开始之前，请确保您的系统满足以下最低要求：\n\n*   **Python**: >= 3.11\n*   **核心依赖**:\n    *   NumPy >= 1.24.1\n    *   SciPy >= 1.10.0\n    *   joblib >= 1.3.0\n    *   threadpoolctl >= 3.2.0\n*   **可选依赖** (用于绘图和运行示例):\n    *   Matplotlib >= 3.6.1\n    *   pandas >= 1.5.0\n    *   scikit-image >= 0.22.0\n    *   seaborn >= 0.13.0\n\n## 安装步骤\n\n如果您已经安装了 working 版本的 NumPy 和 SciPy，推荐使用以下方式进行安装。\n\n### 方式一：使用 pip 安装（推荐国内用户配置镜像）\n\n使用官方源：\n```bash\npip install -U scikit-learn\n```\n\n**国内加速方案**：建议使用清华大学或阿里云镜像源以提升下载速度：\n```bash\npip install -U scikit-learn -i https:\u002F\u002Fpypi.tuna.tsinghua.edu.cn\u002Fsimple\n# 或者\npip install -U scikit-learn -i https:\u002F\u002Fmirrors.aliyun.com\u002Fpypi\u002Fsimple\u002F\n```\n\n### 方式二：使用 conda 安装\n\n如果您使用 Anaconda 或 Miniconda 环境：\n```bash\nconda install -c conda-forge scikit-learn\n```\n\n## 基本使用\n\nscikit-learn 的核心设计理念是统一的 API。以下是一个最简单的分类模型训练与预测示例：\n\n```python\nfrom sklearn.datasets import load_iris\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import accuracy_score\n\n# 1. 加载数据集\niris = load_iris()\nX, y = iris.data, iris.target\n\n# 2. 划分训练集和测试集\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# 3. 初始化并训练模型\nclf = RandomForestClassifier(n_estimators=100, random_state=42)\nclf.fit(X_train, y_train)\n\n# 4. 进行预测\ny_pred = clf.predict(X_test)\n\n# 5. 评估模型\nprint(f\"Accuracy: {accuracy_score(y_test, y_pred):.2f}\")\n```\n\n更多详细文档和高级用法请访问官方网站：https:\u002F\u002Fscikit-learn.org","某电商公司的数据分析师需要基于用户历史行为数据，快速构建一个模型来预测哪些用户会在下个月流失，以便运营团队及时干预。\n\n### 没有 scikit-learn 时\n- **算法实现困难**：工程师需从零编写逻辑回归或随机森林的数学公式与梯度下降代码，极易出错且耗时数周。\n- **流程割裂繁琐**：数据清洗、特征标准化、模型训练和评估分散在不同脚本中，缺乏统一接口，导致代码难以维护。\n- **调参效率低下**：缺乏内置的网格搜索（Grid Search）工具，手动尝试不同参数组合如同“大海捞针”，严重拖慢迭代速度。\n- **结果复现性差**：由于缺少标准化的预处理管道（Pipeline），每次实验环境微小变动都可能导致模型效果波动，难以向业务方交付稳定结果。\n\n### 使用 scikit-learn 后\n- **开箱即用算法**：直接调用 `RandomForestClassifier` 等成熟模块，几行代码即可加载高性能算法，将开发周期从数周缩短至几小时。\n- **统一工作流**：利用 `Pipeline` 将数据标准化与模型训练串联，确保处理逻辑一致，代码结构清晰且易于扩展。\n- **自动化调优**：通过 `GridSearchCV` 自动遍历最佳参数组合，并自带交叉验证功能，显著提升模型准确率与泛化能力。\n- **标准化评估**：内置丰富的评估指标（如 ROC 曲线、混淆矩阵）和可视化工具，一键生成专业报告，让业务决策更有据可依。\n\nscikit-learn 将复杂的机器学习工程简化为标准的模块化操作，让团队能专注于业务洞察而非底层算法实现。","https:\u002F\u002Foss.gittoolsai.com\u002Fimages\u002Fscikit-learn_scikit-learn_65f6c023.png","https:\u002F\u002Foss.gittoolsai.com\u002Favatars\u002Fscikit-learn_42578a15.png","Repositories related to the scikit-learn Python machine learning library.",null,"http:\u002F\u002Fscikit-learn.org","https:\u002F\u002Fgithub.com\u002Fscikit-learn",[82,86,90,94,98,101,105,109,113],{"name":83,"color":84,"percentage":85},"Python","#3572A5",92.7,{"name":87,"color":88,"percentage":89},"Cython","#fedf5b",5.3,{"name":91,"color":92,"percentage":93},"C++","#f34b7d",1,{"name":95,"color":96,"percentage":97},"Shell","#89e051",0.3,{"name":99,"color":100,"percentage":97},"C","#555555",{"name":102,"color":103,"percentage":104},"Meson","#007800",0.2,{"name":106,"color":107,"percentage":108},"CSS","#663399",0.1,{"name":110,"color":111,"percentage":112},"JavaScript","#f1e05a",0,{"name":114,"color":115,"percentage":112},"Makefile","#427819",65628,26870,"2026-04-05T10:10:46","BSD-3-Clause","未说明","不需要 GPU",{"notes":123,"python":124,"dependencies":125},"绘图功能需要 Matplotlib>=3.6.1；运行部分示例可能需要 scikit-image>=0.22.0、pandas>=1.5.0、seaborn>=0.13.0 或 Plotly>=5.18.0。测试需 pytest>=7.1.2。推荐使用 pip 或 conda 安装。","3.11+",[126,127,128,129],"NumPy>=1.24.1","SciPy>=1.10.0","joblib>=1.3.0","threadpoolctl>=3.2.0",[13,54,51],[132,133,134,135,136],"machine-learning","python","statistics","data-science","data-analysis",52,"2026-03-27T02:49:30.150509","2026-04-06T05:15:38.752341",[141,146,151,156,161,165],{"id":142,"question_zh":143,"answer_zh":144,"source_url":145},10302,"如何为 scikit-learn 的公共函数添加参数验证？","需要使用 `sklearn.utils._param_validation.validate_params` 装饰器来验证函数参数。具体步骤如下：\n1. 选择一个在文档中列出的公共函数，检查源代码中是否已有手动参数验证。\n2. 使用装饰器修饰该函数，传入一个字典，键为参数名，值为对应的约束条件。不要仅依赖文档字符串，应主要依据实现代码来确定有效值。\n3. 移除所有现有的简单参数验证代码（即不依赖输入数据或其他参数值的验证）。\n4. 提交 PR 时，标题格式应为 `MAINT Parameters validation for \u003Cfunction>`，描述应以 `Towards #24862` 开头。","https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fissues\u002F24862",{"id":147,"question_zh":148,"answer_zh":149,"source_url":150},10303,"遇到 numpydoc 验证错误 \"YD01: No Yields section found\" 该如何解决？","这通常是由 `cv` 参数的文档字符串中包含 \"yielding\" 一词引起的。解决方法是将描述更新为列表格式。例如，将 `An iterable yielding (train, test) splits as arrays of indices` 修改为：\n`- An iterable that generates (train, test) splits as arrays of indices.`\n此外，如果使用的是 numpydoc 1.2 版本，即使修改了代码也可能报错，建议将 numpydoc 升级到 1.3.1 或更高版本以彻底解决此问题。","https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fissues\u002F21350",{"id":152,"question_zh":153,"answer_zh":154,"source_url":155},10304,"如何确保估算器（Estimator）的文档字符串通过 numpydoc 验证？","请遵循以下步骤：\n1. 安装开发依赖和文档依赖。\n2. 从任务列表中选择一个估算器，并在 Issue 下留言声明你正在处理它，以避免重复工作。\n3. 从测试文件的忽略列表中移除该估算器。\n4. 运行测试命令：`pytest maint_tools\u002Ftest_docstrings.py -k \u003CEstimatorName>-`（注意名称后加连字符）。\n5. 根据测试失败提供的建议修复文档字符串。\n6. 提交单独的 Pull Request，标题格式示例：\"DOC Ensures that StandardScaler passes numpydoc validation\"，并在开头注明 `Addresses #20308`。","https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fissues\u002F20308",{"id":157,"question_zh":158,"answer_zh":159,"source_url":160},10305,"如何为 scikit-learn 适配 scipy.sparse 数组（array）的测试？","SciPy 稀疏矩阵（matrix）已有测试，但稀疏数组（array，SciPy >= 1.8 支持）尚未覆盖。适配步骤如下：\n1. 在此 Issue 中声明你要处理的测试文件，防止他人重复工作。\n2. 参考已有的 PR（如 #27095）作为示例。\n3. 修改测试文件及其参数化设置，使其在 SciPy >= 1.8 的版本条件下，也能包含对 `scipy.sparse.*array` 类型的测试。\n4. 确保测试逻辑能同时兼容 matrix 和 array 类型。","https:\u002F\u002Fgithub.com\u002Fscikit-learn\u002Fscikit-learn\u002Fissues\u002F27090",{"id":162,"question_zh":163,"answer_zh":164,"source_url":145},10306,"哪些公共函数目前尚未进行参数验证，或者被认为不需要验证？","根据维护者的讨论，以下函数虽为公共函数但可能不需要添加参数验证，因为它们更适合作为开发 API 而非用户 API：\n- `base.clone`\n- `base.is_classifier`\n- `base.is_regressor`\n- `config_context`\n对于其他未验证的函数（如 `decomposition.dict_learning_online` 等），通常需要通过单独的 PR 逐个添加验证。如果在函数中发现没有任何验证逻辑，可以在相关 Issue 中报告以决定是否跳过。",{"id":166,"question_zh":167,"answer_zh":168,"source_url":145},10307,"在贡献文档修复时，如何正确命名 Pull Request 和编写描述？","为了便于追踪和管理，请遵循以下规范：\n1. **PR 标题**：必须包含所处理的函数或估算器名称。例如，对于参数验证使用 `MAINT Parameters validation for \u003Cfunction>`；对于文档验证使用 `DOC Ensures that \u003CEstimator> passes numpydoc validation`。\n2. **PR 描述**：必须以特定的关联语句开头。例如，针对参数验证项目，描述需以 `Towards #24862` 开头；针对文档验证项目，需以 `Addresses #20308` 或相应 Issue 编号开头。\n3. **独立性**：每个函数或估算器的修复应提交为独立的 PR，不要合并多个修复。",[170,175,180,185,190,195,200,205,210,215,220,225,230,235,240,245,250,255,260,265],{"id":171,"version":172,"summary_zh":173,"released_at":174},107528,"1.8.0","We're happy to announce the 1.8.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_8_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.8.html\r\n\r\nThis version supports Python versions 3.11 to 3.14 and features support of free-threaded CPython.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2025-12-10T13:32:02",{"id":176,"version":177,"summary_zh":178,"released_at":179},107529,"1.7.2","We're happy to announce the 1.7.2 release.\r\n\r\nThis release contains a few bug fixes and is the first version supporting Python 3.14.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.7.html#version-1-7-2\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```\r\n\r\nThanks to everyone who contributed to this release !","2025-09-09T08:57:24",{"id":181,"version":182,"summary_zh":183,"released_at":184},107530,"1.7.1","We're happy to announce the 1.7.1 release.\r\n\r\nThis release contains fixes for a few regressions introduced in 1.7.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.7.html#version-1-7-1\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```\r\n\r\nThanks to everyone who contributed to this release !","2025-07-18T09:24:49",{"id":186,"version":187,"summary_zh":188,"released_at":189},107531,"1.7.0","We're happy to announce the 1.7.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_7_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.7.html\r\n\r\nThis version supports Python versions 3.10 to 3.13 and features an experimental support of free-threaded CPython.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2025-06-06T08:24:16",{"id":191,"version":192,"summary_zh":193,"released_at":194},107532,"1.6.1","We're happy to announce the 1.6.1 release.\r\n\r\nThis release contains fixes for a few regressions introduced in 1.6.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.6.html#version-1-6-1\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```\r\n\r\nThanks to everyone who contributed to this release !","2025-01-10T10:51:11",{"id":196,"version":197,"summary_zh":198,"released_at":199},107533,"1.6.0","We're happy to announce the 1.6.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_6_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.6.html\r\n\r\nThis version supports Python versions 3.9 to 3.13 and features an experimental support of free-threaded CPython.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2024-12-09T18:30:05",{"id":201,"version":202,"summary_zh":203,"released_at":204},107534,"1.5.2","We're happy to announce the 1.5.2 release.\r\n\r\nThis release contains fixes for a few regressions introduced in 1.5.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.5.html#version-1-5-2\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```\r\n\r\nThanks to everyone who contributed to this release !","2024-09-11T15:52:05",{"id":206,"version":207,"summary_zh":208,"released_at":209},107535,"1.5.1","We're happy to announce the 1.5.1 release.\r\n\r\nThis release contains fixes for a few regressions introduced in 1.5.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.5.html#version-1-5-1\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```\r\n\r\nThanks to everyone who contributed to this release !","2024-07-03T09:17:35",{"id":211,"version":212,"summary_zh":213,"released_at":214},107536,"1.5.0","We're happy to announce the 1.5.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_5_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.5.html\r\n\r\nThis version supports Python versions 3.9 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2024-05-21T16:37:08",{"id":216,"version":217,"summary_zh":218,"released_at":219},107537,"1.4.2","We're happy to announce the 1.4.2 release.\r\n\r\nThis release only includes support for numpy 2.\r\n\r\nThis version supports Python versions 3.9 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```","2024-04-09T20:07:22",{"id":221,"version":222,"summary_zh":223,"released_at":224},107538,"1.4.1.post1","We're happy to announce the 1.4.1.post1 release.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.4.html#version-1-4-1-post1\r\n\r\nThis version supports Python versions 3.9 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2024-02-15T16:07:32",{"id":226,"version":227,"summary_zh":228,"released_at":229},107539,"1.4.1","We're happy to announce the 1.4.1 release.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.4.html#version-1-4-1\r\n\r\nThis version supports Python versions 3.9 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2024-02-14T10:12:43",{"id":231,"version":232,"summary_zh":233,"released_at":234},107540,"1.4.0-1","We're happy to announce the 1.4.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_4_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.4.html\r\n\r\nThis version supports Python versions 3.9 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2024-01-19T10:57:04",{"id":236,"version":237,"summary_zh":238,"released_at":239},107541,"1.3.2","We're happy to announce the 1.3.2 release.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.3.html#version-1-3-2\r\n\r\nThis version supports Python versions 3.8 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2023-10-25T08:23:32",{"id":241,"version":242,"summary_zh":243,"released_at":244},107542,"1.3.1","We're happy to announce the 1.3.1 release.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.3.html#version-1-3-1\r\n\r\nThis version supports Python versions 3.8 to 3.12.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2023-09-20T14:04:16",{"id":246,"version":247,"summary_zh":248,"released_at":249},107543,"1.3.0","We're happy to announce the 1.3.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_3_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.3.html\r\n\r\nThis version supports Python versions 3.8 to 3.11.\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds can be installed using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2023-06-30T08:07:27",{"id":251,"version":252,"summary_zh":253,"released_at":254},107544,"1.2.2","We're happy to announce the 1.2.2 release.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.2.html#version-1-2-2\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds will be available shortly, which you can then install using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2023-03-09T10:08:59",{"id":256,"version":257,"summary_zh":258,"released_at":259},107545,"1.2.1","We're happy to announce the 1.2.1 release.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.2.html#version-1-2-1\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds will be available shortly, which you can then install using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2023-01-24T16:52:14",{"id":261,"version":262,"summary_zh":263,"released_at":264},107546,"1.2.0","We're happy to announce the 1.2.0 release.\r\n\r\nYou can read the release highlights under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fauto_examples\u002Frelease_highlights\u002Fplot_release_highlights_1_2_0.html and the long version of the change log under https:\u002F\u002Fscikit-learn.org\u002Fstable\u002Fwhats_new\u002Fv1.2.html\r\n\r\nThis version supports Python versions 3.8 to 3.11.","2022-12-08T14:17:42",{"id":266,"version":267,"summary_zh":268,"released_at":269},107547,"1.1.3","We're happy to announce the 1.1.3 release.\r\n\r\nThis bugfix release only includes fixes for compatibility with the latest SciPy release >= 1.9.2 and wheels for Python 3.11. Note that support for 32-bit Python on Windows has been dropped in this release. This is due to the fact that SciPy 1.9.2 also dropped the support for that platform. Windows users are advised to install the 64-bit version of Python instead.\r\n\r\nYou can see the changelog here: https:\u002F\u002Fscikit-learn.org\u002Fdev\u002Fwhats_new\u002Fv1.1.html#version-1-1-3\r\n\r\nYou can upgrade with pip as usual:\r\n\r\n```\r\npip install -U scikit-learn\r\n```\r\n\r\nThe conda-forge builds will be available shortly, which you can then install using:\r\n\r\n```\r\nconda install -c conda-forge scikit-learn\r\n```","2022-10-26T14:44:14"]