{"id":13435387,"url":"https://github.com/apachecn/sklearn-doc-zh","last_synced_at":"2025-05-14T04:09:11.891Z","repository":{"id":38361670,"uuid":"104967369","full_name":"apachecn/sklearn-doc-zh","owner":"apachecn","description":":book: [译] scikit-learn（sklearn） 中文文档","archived":false,"fork":false,"pushed_at":"2023-07-21T08:48:33.000Z","size":63235,"stargazers_count":5165,"open_issues_count":10,"forks_count":1471,"subscribers_count":207,"default_branch":"master","last_synced_at":"2025-04-10T22:30:46.365Z","etag":null,"topics":["documentation","machine-learning","python","scikit-learn"],"latest_commit_sha":null,"homepage":"http://sklearn.apachecn.org","language":"CSS","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/apachecn.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":"CONTRIBUTING.md","funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2017-09-27T03:25:03.000Z","updated_at":"2025-04-10T00:54:40.000Z","dependencies_parsed_at":"2023-10-20T17:30:09.531Z","dependency_job_id":null,"html_url":"https://github.com/apachecn/sklearn-doc-zh","commit_stats":{"total_commits":212,"total_committers":25,"mean_commits":8.48,"dds":0.7688679245283019,"last_synced_commit":"6fbef7c2850dd509b958c49a9e0fc98de968ef20"},"previous_names":[],"tags_count":1,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fsklearn-doc-zh","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fsklearn-doc-zh/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fsklearn-doc-zh/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/apachecn%2Fsklearn-doc-zh/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/apachecn","download_url":"https://codeload.github.com/apachecn/sklearn-doc-zh/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":254070020,"owners_count":22009559,"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":["documentation","machine-learning","python","scikit-learn"],"created_at":"2024-07-31T03:00:35.324Z","updated_at":"2025-05-14T04:09:06.852Z","avatar_url":"https://github.com/apachecn.png","language":"CSS","readme":"# \u003ccenter\u003escikit-learn (sklearn) 官方文档中文版\u003c/center\u003e\n\n\u003ccenter\u003e\u003cimg src=\"img/logo/scikit-learn-logo.png\" alt=\"logo\" /\u003e\u003c/center\u003e\n\n\u003cbr/\u003e\n\u003ctable\u003e\n    \u003ctr align=\"center\"\u003e\n        \u003ctd\u003e\u003ca title=\"sklearn 0.21.3[master] 中文文档\" href=\"https://sklearn.apachecn.org/\" target=\"_blank\"\u003e\u003cfont size=\"5\"\u003esklearn 0.21.3 中文文档\u003c/font\u003e\u003c/a\u003e\u003c/td\u003e\n        \u003ctd\u003e\u003ca title=\"sklearn 0.21.3[master] 中文示例\" href=\"https://sklearn.apachecn.org/docs/examples\" target=\"_blank\"\u003e\u003cfont size=\"5\"\u003esklearn 0.21.3 中文示例\u003c/font\u003e\u003c/a\u003e\u003c/td\u003e\n        \u003ctd\u003e\u003ca title=\"sklearn 英文官网\" href=\"https://scikit-learn.org\" target=\"_blank\"\u003e\u003cfont size=\"5\"\u003esklearn 英文官网\u003c/font\u003e\u003c/a\u003e\u003c/td\u003e\n    \u003c/tr\u003e\n\u003c/table\u003e\n\u003cbr/\u003e\n\n---\n\n## 介绍\n\nsklearn (scikit-learn) 是基于 Python 语言的机器学习工具\n\n1. 简单高效的数据挖掘和数据分析工具\n2. 可供大家在各种环境中重复使用\n3. 建立在 NumPy ，SciPy 和 matplotlib 上\n4. 开源，可商业使用 - BSD许可证\n\n\u003e 组织构建[网站]\n\n+ GitHub Pages(国外): https://sklearn.apachecn.org\n+ Gitee Pages(国内): https://apachecn.gitee.io/sklearn-doc-zh\n\n\u003e 第三方站长[网站]\n\n+ 地址A: xxx (欢迎留言，我们完善补充)\n\n\u003e 其他补充\n\n+ [官方Github](https://github.com/apachecn/scikit-learn-doc-zh)\n+ [EPUB 下载地址](https://github.com/apachecn/sklearn-doc-zh/raw/epub/sklearn_0.21.3_2019_12_13.epub)\n+ [ApacheCN 翻译校对兼职群 713436582](https://jq.qq.com/?_wv=1027\u0026k=VSNtgpjb)\n\n## 下载\n\n### Docker\n\n```\ndocker pull apachecn0/sklearn-doc-zh\ndocker run -tid -p \u003cport\u003e:80 apachecn0/sklearn-doc-zh\n# 访问 http://localhost:{port} 查看文档\n```\n\n### PYPI\n\n```\npip install sklearn-doc-zh\nsklearn-doc-zh \u003cport\u003e\n# 访问 http://localhost:{port} 查看文档\n```\n\n### NPM\n\n```\nnpm install -g sklearn-doc-zh\nsklearn-doc-zh \u003cport\u003e\n# 访问 http://localhost:{port} 查看文档\n```\n\n## 目录\n\n*   [安装 scikit-learn](docs/master/62.md)\n*   用户指南\n    *   [1. 监督学习](docs/master/1.md)\n        * [1.1. 广义线性模型](docs/master/2.md)\n        * [1.2. 线性和二次判别分析](docs/master/3.md)\n        * [1.3. 内核岭回归](docs/master/4.md)\n        * [1.4. 支持向量机](docs/master/5.md)\n        * [1.5. 随机梯度下降](docs/master/6.md)\n        * [1.6. 最近邻](docs/master/7.md)\n        * [1.7. 高斯过程](docs/master/8.md)\n        * [1.8. 交叉分解](docs/master/9.md)\n        * [1.9. 朴素贝叶斯](docs/master/10.md)\n        * [1.10. 决策树](docs/master/11.md)\n        * [1.11. 集成方法](docs/master/12.md)\n        * [1.12. 多类和多标签算法](docs/master/13.md)\n        * [1.13. 特征选择](docs/master/14.md)\n        * [1.14. 半监督学习](docs/master/15.md)\n        * [1.15. 等式回归](docs/master/16.md)\n        * [1.16. 概率校准](docs/master/17.md)\n        * [1.17. 神经网络模型（有监督）](docs/master/18.md)\n    *   [2. 无监督学习](docs/master/19.md)\n        * [2.1. 高斯混合模型](docs/master/20.md)\n        * [2.2. 流形学习](docs/master/21.md)\n        * [2.3. 聚类](docs/master/22.md)\n        * [2.4. 双聚类](docs/master/23.md)\n        * [2.5. 分解成分中的信号（矩阵分解问题）](docs/master/24.md)\n        * [2.6. 协方差估计](docs/master/25.md)\n        * [2.7. 新奇和异常值检测](docs/master/26.md)\n        * [2.8. 密度估计](docs/master/27.md)\n        * [2.9. 神经网络模型（无监督）](docs/master/28.md)\n    * [3. 模型选择和评估](docs/master/29.md)\n        * [3.1. 交叉验证：评估估算器的表现](docs/master/30.md)\n        * [3.2. 调整估计器的超参数](docs/master/31.md)\n        * [3.3. 模型评估: 量化预测的质量](docs/master/32.md)\n        * [3.4. 模型持久化](docs/master/33.md)\n        * [3.5. 验证曲线: 绘制分数以评估模型](docs/master/34.md)\n    * [4.  检验](docs/master/35.md)\n        * [4.1. 部分依赖图](docs/master/36.md)\n    * [5. 数据集转换](docs/master/37.md)\n        * [5.1. Pipeline（管道）和 FeatureUnion（特征联合）: 合并的评估器](docs/master/38.md)\n        * [5.2. 特征提取](docs/master/39.md)\n        * [5.3 预处理数据](docs/master/40.md)\n        * [5.4 缺失值插补](docs/master/41.md)\n        * [5.5. 无监督降维](docs/master/42.md)\n        * [5.6. 随机投影](docs/master/43.md)\n        * [5.7. 内核近似](docs/master/44.md)\n        * [5.8. 成对的矩阵, 类别和核函数](docs/master/45.md)\n        * [5.9. 预测目标 (`y`) 的转换](docs/master/46.md)\n    * [6. 数据集加载工具](docs/master/47.md)\n        * [6.1. 通用数据集 API](docs/master/47.md)\n        * [6.2. 玩具数据集](docs/master/47.md)\n        * [6.3 真实世界中的数据集](docs/master/47.md)\n        * [6.4. 样本生成器](docs/master/47.md)\n        * [6.5. 加载其他数据集](docs/master/47.md)\n    * [7. 使用scikit-learn计算](docs/master/48.md)\n        * [7.1. 大规模计算的策略: 更大量的数据](docs/master/48.md)\n        * [7.2. 计算性能](docs/master/48.md)\n        * [7.3. 并行性、资源管理和配置](docs/master/48.md)\n*   [教程](docs/master/50.md)\n    *   [使用 scikit-learn 介绍机器学习](docs/master/51.md)\n    *   [关于科学数据处理的统计学习教程](docs/master/52.md)\n        *   [机器学习: scikit-learn 中的设置以及预估对象](docs/master/53.md)\n        *   [监督学习：从高维观察预测输出变量](docs/master/54.md)\n        *   [模型选择：选择估计量及其参数](docs/master/55.md)\n        *   [无监督学习: 寻求数据表示](docs/master/56.md)\n        *   [把它们放在一起](docs/master/57.md)\n        *   [寻求帮助](docs/master/58.md)\n    *   [处理文本数据](docs/master/59.md)\n    *   [选择正确的评估器(estimator.md)](docs/master/60.md)\n    *   [外部资源，视频和谈话](docs/master/61.md)\n*   [API 参考](https://scikit-learn.org/stable/modules/classes.html)\n*   [常见问题](docs/master/63.md)\n*   [时光轴](docs/master/64.md)\n\n## 历史版本\n\n* [scikit-learn (sklearn) 0.19 官方文档中文版](https://github.com/apachecn/sklearn-doc-zh/tree/master/docs/0.19.x.zip)\n* [scikit-learn (sklearn) 0.18 官方文档中文版](http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181)\n\n如何编译使用历史版本: \n\n* 解压 `0.19.x.zip` 文件夹\n* 将 `master/img` 的图片资源, 复制到 `0.19.x` 里面去\n* gitbook 正常编译过程，可以使用 `sh run_website.sh`\n\n## 贡献指南\n\n为了不断改进翻译质量，我们特此启动了【翻译、校对、笔记整理活动】，开设了多个校对项目。贡献者校对一章之后可以领取千字2\\~4元的奖励。进行中的校对活动请见[活动列表](https://home.apachecn.org/#/docs/activity/docs-activity)。更多详情请联系飞龙（Q562826179，V:wizardforcel）。\n\n\n## DOCX：开放共享科研记录行动倡议\n\n我们积极响应[科研开源计划（DOCX）](https://mmcheng.net/docx/)。如今开源不仅仅是开放源码，还包括数据集、模型、教程和实验记录。我们也在探讨其它类别的开源方案和协议。\n\n希望大家了解这个倡议，把这个倡议与自己的兴趣点结合，做点力所能及的事情。每个人的微小的贡献，汇聚在一起就是整个开源生态。\n\n## 项目负责人\n\n格式: GitHub + QQ\n\n\u003e 第一期 (2017-09-29)\n\n* [@那伊抹微笑](https://github.com/wangyangting)\n* [@片刻](https://github.com/jiangzhonglian)\n* [@小瑶](https://github.com/chenyyx)\n\n\u003e 第二期 (2019-06-29)\n\n* [@N!no](https://github.com/lovelybuggies)：1352899627\n* [@mahaoyang](https://github.com/mahaoyang)：992635910\n* [@loopyme](https://github.com/loopyme)：3322728009\n* [飞龙](https://github.com/wizardforcel)：562826179\n* [片刻](https://github.com/jiangzhonglian)：529815144\n\n-- 负责人要求: (欢迎一起为 `sklearn 中文版本` 做贡献)\n\n* 热爱开源，喜欢装逼\n* 长期使用 sklearn(至少0.5年) + 提交Pull Requests\u003e=3\n* 能够有时间及时优化页面 bug 和用户 issues\n* 试用期: 2个月\n* 欢迎联系: [片刻](https://github.com/jiangzhonglian) 529815144\n\n## 贡献者\n\n[【0.19.X】贡献者名单](https://github.com/apachecn/sklearn-doc-zh/issues/354)\n\n## 建议反馈\n\n* 在我们的 [apachecn/pytorch-doc-zh](https://github.com/apachecn/sklearn-doc-zh) github 上提 issue.\n* 发邮件到 Email: `apachecn@163.com`.\n* 在我们的 [QQ群-搜索: 交流方式](https://github.com/apachecn/home) 中联系群主/管理员即可.\n\n## **项目协议**\n\n* **最近有很多人联系我们，关于内容授权问题！**\n* 开源是指知识应该重在传播和迭代（而不是禁止别人转载）\n* 不然你TM在GitHub开源，然后又说不让转载，你TM有病吧！\n* 禁止商业化，符合协议规范，备注地址来源，**重点: 不需要**发邮件给我们申请\n* ApacheCN 账号下没有协议的项目，一律视为 [CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/deed.zh)。\n\n温馨提示:\n\n* 对于个人想自己copy一份再更新的人\n* 我也是有这样的经历，但是这种激情维持不了几个月，就泄气了！\n* 不仅浪费了你的心血，还浪费了更多人看到你的翻译成果！很可惜！你觉得呢？\n* 个人的建议是: fork -\u003e pull requests 到 `https://github.com/apachecn/sklearn-doc-zh`\n* 那为什么要选择 `ApacheCN` 呢？\n* 因为我们做翻译这事情是觉得开心和装逼，比较纯粹！\n* 你如果喜欢，你可以来参与/甚至负责这个项目，没有任何学历和背景的限制\n\n## 赞助我们\n\n\u003cimg src=\"http://data.apachecn.org/img/about/donate.jpg\" alt=\"微信\u0026支付宝\" /\u003e\n","funding_links":[],"categories":["1 Predict","CSS","语言资源库","A01_机器学习教程"],"sub_categories":["Machine Learning Related Documention","books"],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapachecn%2Fsklearn-doc-zh","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fapachecn%2Fsklearn-doc-zh","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fapachecn%2Fsklearn-doc-zh/lists"}