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118 changes: 118 additions & 0 deletions .gitignore
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# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class

# C extensions
*.so

# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib/
lib64/
parts/
sdist/
var/
wheels/
*.egg-info/
.installed.cfg
*.egg

# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec

# Installer logs
pip-log.txt
pip-delete-this-directory.txt

# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*.cover
.hypothesis/

# Translations
*.mo
*.pot

# Django stuff:
*.log
local_settings.py

# Flask stuff:
instance/
.webassets-cache

# Scrapy stuff:
.scrapy

# Sphinx documentation
docs/_build/

# PyBuilder
target/

# Jupyter Notebook
.ipynb_checkpoints

# pyenv
.python-version

# celery beat schedule file
celerybeat-schedule

# SageMath parsed files
*.sage.py

# dotenv
.env

# virtualenv
.venv
venv/
ENV/

# Spyder project settings
.spyderproject
.spyproject

# Rope project settings
.ropeproject

# mkdocs documentation
/site

# mypy
.mypy_cache/
.DS_Store

# gitbook
_book

# node.js
node_modules

# windows
Thumbs.db

# word
~$*.docx
~$*.doc

# custom
docs/README.md
Empty file added .nojekyll
Empty file.
21 changes: 21 additions & 0 deletions .travis.yml
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language: python
python: 3.6

install:
- ':'

script:
- ':'

after_script:
- git config user.name ${GH_UN}
- git config user.email ${GH_EMAIL}
- git push "https://${GH_TOKEN}@github.com/${GH_USER}/${GH_REPO}.git" v0.19.x-md:${GH_BRANCH} -f

env:
global:
- GH_UN=jiangzhonglian
- GH_EMAIL=jiang-s@163.com
- GH_USER=apachecn
- GH_REPO=sklearn-doc-zh
- GH_BRANCH=gh-pages
1 change: 1 addition & 0 deletions CNAME
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sklearn.apachecn.org
30 changes: 30 additions & 0 deletions CONTRIBUTING.md
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# 贡献指南

## 参与翻译 & 发现错误

1. 在 github 上 fork 该 repository.
2. 翻译 doc/zh/modules 和 doc/zh/tutorial 下面的 rst 文件即可, 例如, tutorial.rst.
3. 然后, 在你的 github 发起 New pull request 请求.
4. 工具使用, 可参考下面的内容.

## 工具使用(针对新手)

工欲善其事, 必先利其器 ...
工具随意, 能达到效果就好.
我这里使用的是 `VSCode` 编辑器.
简易的使用指南请参阅: [VSCode Windows 平台入门使用指南](help/vscode-windows-usage.md), 介绍了 `VSCode``github` 一起搭配的简易使用的方法.
如果要将 VSCode 的 Markdown 预览风格切换为 github 的风格,请参阅: [VSCode 修改 markdown 的预览风格为 github 的风格](help/vscode-markdown-preview-github-style.md).

**注意注意注意:**

为了尽量正规化各顶级项目的翻译,更便于以后的迭代更新,我们在 `scikit-learn` 文档翻译中使用了 `Git` 的分支,具体应用方法请参阅: [使用 Git 分支进行迭代翻译](help/git-branch-usage.md).

## 角色分配

目前有如下可分配的角色:

* 翻译: 负责文章内容的翻译.
* 校验: 负责文章内容的校验, 比如格式, 正确度之类的.
* 负责人: 负责整个 Projcet, 不至于让该 Project 成为垃圾项目, 需要在 sklearn 方面经验稍微丰富点.

有兴趣参与的朋友, 可以看看最后的联系方式.
136 changes: 136 additions & 0 deletions README.md
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# scikit-learn (sklearn) 官方文档中文版

![](docs/img/scikit-learn-logo.png)

scikit-learn 是基于 Python 语言的机器学习工具。

1. 简单高效的数据挖掘和数据分析工具
2. 可供大家在各种环境中重复使用
3. 建立在 NumPy ,SciPy 和 matplotlib 上
4. 开源,可商业使用 - BSD许可证

## 维护地址

+ [Github](https://github.com/apachecn/scikit-learn-doc-zh/): <>
+ [在线阅读](http://www.apachecn.org)
+ [EPUB 格式](https://github.com/apachecn/scikit-learn-doc-zh/raw/dl/scikit-learn%200.19%20%E4%B8%AD%E6%96%87%E6%96%87%E6%A1%A3.epub)

## 历史版本

* [scikit-learn (sklearn) 0.18 官方文档中文版](http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181): <http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181>

## 贡献指南

[请见这里](CONTRIBUTING.md)

## 负责人

* [@chenyyx](https://github.com/chenyyx)(Joy yx)

## 贡献者

贡献者可自行编辑如下内容(排名不分先后).

### 0.19.0

**翻译者(人人皆大佬~):**

* [@wangyangting](https://github.com/wangyangting)(那伊抹微笑)
* [@jiangzhonglian](https://github.com/jiangzhonglian)(片刻)
* [@chenyyx](https://github.com/chenyyx)(Joy yx)
* [@survivingME](https://github.com/survivingME)(Trembleguy)
* [@mtobeiyf](https://github.com/mtobeiyf)(Fing)
* [@Leon-Jin](https://github.com/Leon-Jin)(Leon晋)
* [@X1aoFang](https://github.com/X1aoFang)(X1aoFang)
* [@loverollcat](https://github.com/loverollcat)(loverollcat)
* [@helloheshee](https://github.com/helloheshee)(helloheshee)
* [@SkyDreamStart](https://github.com/SkyDreamStart)(SkyDreamStart)
* [@HazekiahWon](https://github.com/HazekiahWon)(Hazekiah Wang)
* [@201419](https://github.com/201419)(tree)
* [@busesese](https://github.com/busesese)(wenyi)
* [@chrzc](https://github.com/chrzc)(changzhicheng)
* [@Zoctan](https://github.com/Zoctan)(Zoctan)
* [@JanzenLiu](https://github.com/JanzenLiu)(Janzen_Liu)
* [@charleschou520](https://github.com/charleschou520)(charleschou520)
* [@zhangqiking](https://github.com/zhangqiking)(BigBird)
* [@mikechengwei](https://github.com/mikechengwei)(Mike)
* [@wodekwan](https://github.com/wodekwan)(wodekwan)
* [@sehriff](https://github.com/sehriff)(sehriff)
* [@Kdotm](https://github.com/Kdotm)(kngines)
* [@patientman](https://github.com/patientman)(YuanMingLiu)
* [@DamonDay](https://github.com/DamonDay)(DamonDay)
* [@cszhangbo](https://github.com/cszhangbo)(cszhangbo)
* [@XingxGit](https://github.com/XingxGit)(XingxGit)
* [@KrokYin](https://github.com/KrokYin)(KrokYin)
* [@yw10](https://github.com/yw10)(Andrew Yao)
* [@GlassyWu](https://github.com/GlassyWu)(glassy)
* [@PrivateRookie](https://github.com/PrivateRookie)(PrivateRookie)
* [@linweijiang](https://github.com/linweijiang)(linweijiang)
* [@jiaqiangbandongg](https://github.com/jiaqiangbandongg)(jiaqiangbandongg)
* [@supportingvector](https://github.com/supportingvector)(supportingvector)
* [@stealthsMrs](https://github.com/stealthsMrs)(stealthsMrs)
* [@lhwlhw90](https://github.com/lhwlhw90)(lhwlhw90)
* [@sheepmen](https://github.com/sheepmen)(羊三)
* [@guohao916](https://github.com/guohao916)(guohao_dm)
* [@Daemon-ser](https://github.com/Daemon-ser)(csu_hgq)
* [@wangxiancai](https://github.com/wangxiancai)(BWM-蜜蜂)
* [@夜神月](@qq760514101)(夜神月)
* [@liyangbit](https://github.com/liyangbit)(Lemon)
* [@XuJianzhi](https://github.com/XuJianzhi)(XuJianzhi)

**校验者(人人皆大佬~)**(现在还不齐全,贡献者大佬们可随意修改)

* [@weiyd](https://github.com/weiyd)(尔了个达)
* [@FontTian](https://github.com/FontTian)(FontTian)
* [@XuJianzhi](https://github.com/XuJianzhi)(XuJianzhi)
* [@caopeirui](https://github.com/caopeirui)(Veyron C)
* [@HelloSilicat](https://github.com/HelloSilicat)(Jialiang Pei)
* [@AnybodyHome](https://github.com/AnybodyHome)(loop)
* [@RyanZhiNie](https://github.com/RyanZhiNie)(Zhi Nie)
* [@zehuichen123](https://github.com/zehuichen123)(loveSnowBest)
* [@hlxstc](https://github.com/hlxstc)(Jokers)
* [@NellyLuo](https://github.com/NellyLuo)(NellyLuo)
* [@yuezhao9210](https://github.com/yuezhao9210)(Yue Zhao)
* [@why2lyj](https://github.com/why2lyj)(Snow Wong)
* [@pan8664716](https://github.com/pan8664716)(pan8664716)
* [@xrj](https://github.com/xrj)(xrj)
* [@qvduoduo1997](https://github.com/qvduoduo1997)(qvduoduo1997)
* [@marsjhao](https://github.com/marsjhao)(Zhang Junhao)
* [@tiantian1412](https://github.com/tiantian1412)(tiantian1412)
* [@sheepmen](https://github.com/sheepmen)(羊三)
* [@wangyangting](https://github.com/wangyangting)(那伊抹微笑)
* [@jiangzhonglian](https://github.com/jiangzhonglian)(片刻)
* [@chenyyx](https://github.com/chenyyx)(Joy yx)
* [@mikechengwei](https://github.com/mikechengwei)(Mike)
* [@wangxiancai](https://github.com/wangxiancai)(BWM-蜜蜂)
* [@busesese](https://github.com/busesese)(wenyi)
* [@GlassyWu](https://github.com/GlassyWu)(glassy)
* [@wodekwan](https://github.com/wodekwan)(wodekwan)
* [@gbyy422990](https://github.com/gbyy422990)(Bin GAO)
* [@charleschou520](https://github.com/charleschou520)(charleschou520)
* [@DataMonk2017](https://github.com/DataMonk2017)(Jeremy Zheng)
* [@Lixu518](https://github.com/Lixu518)(Mysry)
* [@linxiamen](https://github.com/linxiamen)(YUNCHUAN LIN)
* [@llcing](https://github.com/llcing)(Mayouji)
* [@KyrieHee](https://github.com/KyrieHee)(HeYun)
* [@ziepenn](https://github.com/ziepenn)(ziepenn)
* [@FAN0521](https://github.com/FAN0521)(FAN0521)
* [@Daemon-ser](https://github.com/Daemon-ser)(csu_hgq)
* [@shaoyy147](https://github.com/shaoyy147)(Y.Shao)
* [@Kdotm](https://github.com/Kdotm)(kngines)
* [@chrzc](https://github.com/chrzc)(changzhicheng)
* [@ywujudy](https://github.com/ywujudy)(ywujudy)
* [@污剑客](https://github.com/apachecn)(污剑客)
* [@爱吹牛逼的小明](https://github.com/apachecn)(爱吹牛逼的小明)
* [@JanzenLiu](https://github.com/JanzenLiu)(Janzen_Liu)
* [@Ecauchy](https://github.com/Ecauchy)(Ecauchy)

### 0.18

请参阅: [http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181](http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181)

## 联系方式

有任何建议反馈, 或想参与文档翻译, 麻烦联系下面的企鹅:

* 企鹅: 1042658081(那伊抹微笑), 190442212(小瑶), 529815144(片刻)
67 changes: 67 additions & 0 deletions SUMMARY.md
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+ [安装 scikit-learn](docs/79.md)
+ 用户指南
+ [1\. 监督学习](docs/1.md)
+ [1.1\. 广义线性模型](docs/2.md)
+ [1.2\. 线性和二次判别分析](docs/3.md)
+ [1.3\. 内核岭回归](docs/4.md)
+ [1.4\. 支持向量机](docs/5.md)
+ [1.5\. 随机梯度下降](docs/6.md)
+ [1.6\. 最近邻](docs/7.md)
+ [1.7\. 高斯过程](docs/8.md)
+ [1.8\. 交叉分解](docs/9.md)
+ [1.9\. 朴素贝叶斯](docs/10.md)
+ [1.10\. 决策树](docs/11.md)
+ [1.11\. 集成方法](docs/12.md)
+ [1.12\. 多类和多标签算法](docs/13.md)
+ [1.13\. 特征选择](docs/14.md)
+ [1.14\. 半监督学习](docs/15.md)
+ [1.15\. 等式回归](docs/16.md)
+ [1.16\. 概率校准](docs/17.md)
+ [1.17\. 神经网络模型(有监督)](docs/18.md)
+ [2\. 无监督学习](docs/19.md)
+ [2.1\. 高斯混合模型](docs/20.md)
+ [2.2\. 流形学习](docs/21.md)
+ [2.3\. 聚类](docs/22.md)
+ [2.4\. 双聚类](docs/23.md)
+ [2.5\. 分解成分中的信号(矩阵分解问题)](docs/24.md)
+ [2.6\. 协方差估计](docs/25.md)
+ [2.7\. 经验协方差](docs/26.md)
+ [2.8\. 收敛协方差](docs/27.md)
+ [2.9\. 稀疏逆协方差](docs/28.md)
+ [2.10\. Robust 协方差估计](docs/29.md)
+ [2.11\. 新奇和异常值检测](docs/30.md)
+ [2.12\. 密度估计](docs/31.md)
+ [2.13\. 神经网络模型(无监督)](docs/32.md)
+ [3\. 模型选择和评估](docs/33.md)
+ [3.1\. 交叉验证:评估估算器的表现](docs/34.md)
+ [3.2\. 调整估计器的超参数](docs/35.md)
+ [3.3\. 模型评估: 量化预测的质量](docs/53.md)
+ [3.4\. 模型持久化](docs/54.md)
+ [3.5\. 验证曲线: 绘制分数以评估模型](docs/55.md)
+ [4\. 数据集转换](docs/56.md)
+ [4.1\. Pipeline(管道)和 FeatureUnion(特征联合): 合并的评估器](docs/57.md)
+ [4.2\. 特征提取](docs/58.md)
+ [4.3\. 预处理数据](docs/59.md)
+ [4.4\. 无监督降维](docs/60.md)
+ [4.5\. 随机投影](docs/61.md)
+ [4.6\. 内核近似](docs/62.md)
+ [4.7\. 成对的矩阵, 类别和核函数](docs/63.md)
+ [4.8\. 预测目标 (`y`.md) 的转换](docs/64.md)
+ [5\. 数据集加载工具](docs/65.md)
+ [6\. 大规模计算的策略: 更大量的数据](docs/66.md)
+ [7\. 计算性能](docs/67.md)
+ 教程
+ [使用 scikit-learn 介绍机器学习](docs/68.md)
+ [关于科学数据处理的统计学习教程](docs/69.md)
+ [机器学习: scikit-learn 中的设置以及预估对象](docs/70.md)
+ [监督学习:从高维观察预测输出变量](docs/71.md)
+ [模型选择:选择估计量及其参数](docs/72.md)
+ [无监督学习: 寻求数据表示](docs/73.md)
+ [把它们放在一起](docs/74.md)
+ [寻求帮助](docs/75.md)
+ [处理文本数据](docs/76.md)
+ [选择正确的评估器(estimator.md)](docs/77.md)
+ [外部资源,视频和谈话](docs/78.md)
+ [API 参考](https://scikit-learn.org/stable/modules/classes.html)
+ [常见问题](docs/80.md)
+ [时光轴](docs/81.md)

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