apacheCNml&dl

Python/sklearn/sklearn-cookbook-zh/.gitignore

@@ -1 +0,0 @@
-Thumbs.db

Python/sklearn/sklearn-cookbook-zh/LICENSE

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-Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License (CC BY-NC-SA 4.0)
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-        B.  indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
-        C.  indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
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-b.  ShareAlike.
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-    1.  The Adapter’s License You apply must be a Creative Commons license with the same License Elements, this version or later, or a BY-NC-SA Compatible License.
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-a.  for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
-b.  if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material, including for purposes of Section 3(b); and
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-d.  Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.

Python/sklearn/sklearn-cookbook-zh/README.md

@@ -1,32 +0,0 @@
-# Scikit-learn 秘籍
-
-> 原书：[Scikit-learn Cookbook](https://www.packtpub.com/big-data-and-business-intelligence/scikit-learn-cookbook)
-> 
-> 协议：[CC BY-NC-SA 4.0](http://creativecommons.org/licenses/by-nc-sa/4.0/)
->  
-> 欢迎任何人参与和完善：一个人可以走的很快，但是一群人却可以走的更远。
-
-+   [ApacheCN 机器学习交流群 629470233](http://shang.qq.com/wpa/qunwpa?idkey=30e5f1123a79867570f665aa3a483ca404b1c3f77737bc01ec520ed5f078ddef)
-+   [ApacheCN 学习资源](http://www.apachecn.org/)
-
-<!--break-->
-
-+ [在线阅读](https://www.gitbook.com/book/wizardforcel/sklearn-cookbook/details)
-+ [PDF格式](https://www.gitbook.com/download/pdf/book/wizardforcel/sklearn-cookbook)
-+ [EPUB格式](https://www.gitbook.com/download/epub/book/wizardforcel/sklearn-cookbook)
-+ [MOBI格式](https://www.gitbook.com/download/mobi/book/wizardforcel/sklearn-cookbook)
-+ [代码仓库](http://git.oschina.net/wizardforcel/sklearn-cb)
-
-## 译者
-
-| | 章节 | 译者 |
-| --- | --- | --- |
-| 1 | 预处理 | [muxuezi](https://muxuezi.github.io/posts/1-premodel-workflow.html) |
-| 2 | 回归 | [muxuezi](https://muxuezi.github.io/posts/2-working-with-linear-models.html) |
-| 3 | 聚类 | [飞龙](https://github.com/wizardforcel) |
-| 4 | 分类 | [飞龙](https://github.com/wizardforcel) |
-| 5 | 后处理 | [飞龙](https://github.com/wizardforcel) |
-
-## 赞助我
-
-![](http://ww1.sinaimg.cn/large/841aea59ly1fx0qnvulnjj2074074747.jpg)

Python/sklearn/sklearn-cookbook-zh/SUMMARY.md

@@ -1,6 +0,0 @@
-+ [Scikit-learn 秘籍](README.md)
-+ [第一章 模型预处理](1.md)
-+ [第二章 处理线性模型](2.md)
-+ [第三章 使用距离向量构建模型](3.md)
-+ [第四章 使用 scikit-learn 对数据分类](4.md)
-+ [第五章 模型后处理](5.md)

Python/sklearn/sklearn-cookbook-zh/styles/ebook.css

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Python/sklearn/sklearn-doc-zh/README.md

@@ -1,216 +0,0 @@
-# <center>scikit-learn (sklearn) 官方文档中文版</center>
-
-<center><img src="img/logo/scikit-learn-logo.png" alt="logo" /></center>
-
-<br/>
-<table>
-    <tr align="center">
-        <td><a title="sklearn 0.21.3[master] 中文文档" href="https://sklearn.apachecn.org/" target="_blank"><font size="5">sklearn 0.21.3 中文文档</font></a></td>
-        <td><a title="sklearn 0.21.3[master] 中文示例" href="https://sklearn.apachecn.org/docs/examples" target="_blank"><font size="5">sklearn 0.21.3 中文示例</font></a></td>
-        <td><a title="sklearn 英文官网" href="https://scikit-learn.org" target="_blank"><font size="5">sklearn 英文官网</font></a></td>
-    </tr>
-</table>
-<br/>
-
----
-
-## 介绍
-
-sklearn (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
-
-> 组织构建[网站]
-
-+ GitHub Pages(国外): https://sklearn.apachecn.org
-+ Gitee Pages(国内): https://apachecn.gitee.io/sklearn-doc-zh
-
-> 第三方站长[网站]
-
-+ sklearn 中文文档: http://www.scikitlearn.com.cn
-+ 地址A: xxx (欢迎留言，我们完善补充)
-
-> 其他补充
-
-+ [官方Github](https://github.com/apachecn/scikit-learn-doc-zh)
-+ [EPUB 下载地址](https://github.com/apachecn/sklearn-doc-zh/raw/epub/sklearn_0.21.3_2019_12_13.epub)
-
-## 下载
-
-### Docker
-
-```
-docker pull apachecn0/sklearn-doc-zh
-docker run -tid -p <port>:80 apachecn0/sklearn-doc-zh
-# 访问 http://localhost:{port} 查看文档
-```
-
-### PYPI
-
-```
-pip install sklearn-doc-zh
-sklearn-doc-zh <port>
-# 访问 http://localhost:{port} 查看文档
-```
-
-### NPM
-
-```
-npm install -g sklearn-doc-zh
-sklearn-doc-zh <port>
-# 访问 http://localhost:{port} 查看文档
-```
-
-## 目录
-
-*   [安装 scikit-learn](docs/master/62.md)
-*   用户指南
-    *   [1. 监督学习](docs/master/1.md)
-        * [1.1. 广义线性模型](docs/master/2.md)
-        * [1.2. 线性和二次判别分析](docs/master/3.md)
-        * [1.3. 内核岭回归](docs/master/4.md)
-        * [1.4. 支持向量机](docs/master/5.md)
-        * [1.5. 随机梯度下降](docs/master/6.md)
-        * [1.6. 最近邻](docs/master/7.md)
-        * [1.7. 高斯过程](docs/master/8.md)
-        * [1.8. 交叉分解](docs/master/9.md)
-        * [1.9. 朴素贝叶斯](docs/master/10.md)
-        * [1.10. 决策树](docs/master/11.md)
-        * [1.11. 集成方法](docs/master/12.md)
-        * [1.12. 多类和多标签算法](docs/master/13.md)
-        * [1.13. 特征选择](docs/master/14.md)
-        * [1.14. 半监督学习](docs/master/15.md)
-        * [1.15. 等式回归](docs/master/16.md)
-        * [1.16. 概率校准](docs/master/17.md)
-        * [1.17. 神经网络模型（有监督）](docs/master/18.md)
-    *   [2. 无监督学习](docs/master/19.md)
-        * [2.1. 高斯混合模型](docs/master/20.md)
-        * [2.2. 流形学习](docs/master/21.md)
-        * [2.3. 聚类](docs/master/22.md)
-        * [2.4. 双聚类](docs/master/23.md)
-        * [2.5. 分解成分中的信号（矩阵分解问题）](docs/master/24.md)
-        * [2.6. 协方差估计](docs/master/25.md)
-        * [2.7. 新奇和异常值检测](docs/master/26.md)
-        * [2.8. 密度估计](docs/master/27.md)
-        * [2.9. 神经网络模型（无监督）](docs/master/28.md)
-    * [3. 模型选择和评估](docs/master/29.md)
-        * [3.1. 交叉验证：评估估算器的表现](docs/master/30.md)
-        * [3.2. 调整估计器的超参数](docs/master/31.md)
-        * [3.3. 模型评估: 量化预测的质量](docs/master/32.md)
-        * [3.4. 模型持久化](docs/master/33.md)
-        * [3.5. 验证曲线: 绘制分数以评估模型](docs/master/34.md)
-    * [4.  检验](docs/master/35.md)
-        * [4.1. 部分依赖图](docs/master/36.md)
-    * [5. 数据集转换](docs/master/37.md)
-        * [5.1. Pipeline（管道）和 FeatureUnion（特征联合）: 合并的评估器](docs/master/38.md)
-        * [5.2. 特征提取](docs/master/39.md)
-        * [5.3 预处理数据](docs/master/40.md)
-        * [5.4 缺失值插补](docs/master/41.md)
-        * [5.5. 无监督降维](docs/master/42.md)
-        * [5.6. 随机投影](docs/master/43.md)
-        * [5.7. 内核近似](docs/master/44.md)
-        * [5.8. 成对的矩阵, 类别和核函数](docs/master/45.md)
-        * [5.9. 预测目标 (`y`) 的转换](docs/master/46.md)
-    * [6. 数据集加载工具](docs/master/47.md)
-        * [6.1. 通用数据集 API](docs/master/47.md)
-        * [6.2. 玩具数据集](docs/master/47.md)
-        * [6.3 真实世界中的数据集](docs/master/47.md)
-        * [6.4. 样本生成器](docs/master/47.md)
-        * [6.5. 加载其他数据集](docs/master/47.md)
-    * [7. 使用scikit-learn计算](docs/master/48.md)
-        * [7.1. 大规模计算的策略: 更大量的数据](docs/master/48.md)
-        * [7.2. 计算性能](docs/master/48.md)
-        * [7.3. 并行性、资源管理和配置](docs/master/48.md)
-*   [教程](docs/master/50.md)
-    *   [使用 scikit-learn 介绍机器学习](docs/master/51.md)
-    *   [关于科学数据处理的统计学习教程](docs/master/52.md)
-        *   [机器学习: scikit-learn 中的设置以及预估对象](docs/master/53.md)
-        *   [监督学习：从高维观察预测输出变量](docs/master/54.md)
-        *   [模型选择：选择估计量及其参数](docs/master/55.md)
-        *   [无监督学习: 寻求数据表示](docs/master/56.md)
-        *   [把它们放在一起](docs/master/57.md)
-        *   [寻求帮助](docs/master/58.md)
-    *   [处理文本数据](docs/master/59.md)
-    *   [选择正确的评估器(estimator.md)](docs/master/60.md)
-    *   [外部资源，视频和谈话](docs/master/61.md)
-*   [API 参考](https://scikit-learn.org/stable/modules/classes.html)
-*   [常见问题](docs/master/63.md)
-*   [时光轴](docs/master/64.md)
-
-## 历史版本
-
-* [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)
-* [scikit-learn (sklearn) 0.18 官方文档中文版](http://cwiki.apachecn.org/pages/viewpage.action?pageId=10030181)
-
-如何编译使用历史版本: 
-
-* 解压 `0.19.x.zip` 文件夹
-* 将 `master/img` 的图片资源, 复制到 `0.19.x` 里面去
-* gitbook 正常编译过程，可以使用 `sh run_website.sh`
-
-## 贡献指南
-
-项目当前处于校对阶段，请查看[贡献指南](CONTRIBUTING.md)，并在[整体进度](https://github.com/apachecn/sklearn-doc-zh/issues/352)中领取任务。
-
-> 请您勇敢地去翻译和改进翻译。虽然我们追求卓越，但我们并不要求您做到十全十美，因此请不要担心因为翻译上犯错——在大部分情况下，我们的服务器已经记录所有的翻译，因此您不必担心会因为您的失误遭到无法挽回的破坏。（改编自维基百科）
-
-## 项目负责人
-
-格式: GitHub + QQ
-
-> 第一期 (2017-09-29)
-
-* [@那伊抹微笑](https://github.com/wangyangting)
-* [@片刻](https://github.com/jiangzhonglian)
-* [@小瑶](https://github.com/chenyyx)
-
-> 第二期 (2019-06-29)
-
-* [@N!no](https://github.com/lovelybuggies)：1352899627
-* [@mahaoyang](https://github.com/mahaoyang)：992635910
-* [@loopyme](https://github.com/loopyme)：3322728009
-* [飞龙](https://github.com/wizardforcel)：562826179
-* [片刻](https://github.com/jiangzhonglian)：529815144
-
--- 负责人要求: (欢迎一起为 `sklearn 中文版本` 做贡献)
-
-* 热爱开源，喜欢装逼
-* 长期使用 sklearn(至少0.5年) + 提交Pull Requests>=3
-* 能够有时间及时优化页面 bug 和用户 issues
-* 试用期: 2个月
-* 欢迎联系: [片刻](https://github.com/jiangzhonglian) 529815144
-
-## 贡献者
-
-[【0.19.X】贡献者名单](https://github.com/apachecn/sklearn-doc-zh/issues/354)
-
-## 建议反馈
-
-* 在我们的 [apachecn/pytorch-doc-zh](https://github.com/apachecn/sklearn-doc-zh) github 上提 issue.
-* 发邮件到 Email: `apachecn@163.com`.
-* 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.
-
-## **项目协议**
-
-* **最近有很多人联系我们，关于内容授权问题！**
-* 开源是指知识应该重在传播和迭代（而不是禁止别人转载）
-* 不然你TM在GitHub开源，然后又说不让转载，你TM有病吧！
-* 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
-* 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)。
-
-温馨提示:
-
-* 对于个人想自己copy一份再更新的人
-* 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！
-* 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？
-* 个人的建议是: fork -> pull requests 到 `https://github.com/apachecn/sklearn-doc-zh`
-* 那为什么要选择 `ApacheCN` 呢？
-* 因为我们做翻译这事情是觉得开心和装逼，比较纯粹！
-* 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
-
-## 赞助我们
-
-<img src="http://data.apachecn.org/img/about/donate.jpg" alt="微信&支付宝" />

Python/sklearn/sklearn-doc-zh/SUMMARY.md

@@ -1,73 +0,0 @@
-*   [安装 scikit-learn](docs/master/62.md)
-*   用户指南
-    *   [1. 监督学习](docs/master/1.md)
-        * [1.1. 广义线性模型](docs/master/2.md)
-        * [1.2. 线性和二次判别分析](docs/master/3.md)
-        * [1.3. 内核岭回归](docs/master/4.md)
-        * [1.4. 支持向量机](docs/master/5.md)
-        * [1.5. 随机梯度下降](docs/master/6.md)
-        * [1.6. 最近邻](docs/master/7.md)
-        * [1.7. 高斯过程](docs/master/8.md)
-        * [1.8. 交叉分解](docs/master/9.md)
-        * [1.9. 朴素贝叶斯](docs/master/10.md)
-        * [1.10. 决策树](docs/master/11.md)
-        * [1.11. 集成方法](docs/master/12.md)
-        * [1.12. 多类和多标签算法](docs/master/13.md)
-        * [1.13. 特征选择](docs/master/14.md)
-        * [1.14. 半监督学习](docs/master/15.md)
-        * [1.15. 等式回归](docs/master/16.md)
-        * [1.16. 概率校准](docs/master/17.md)
-        * [1.17. 神经网络模型（有监督）](docs/master/18.md)
-    *   [2. 无监督学习](docs/master/19.md)
-        * [2.1. 高斯混合模型](docs/master/20.md)
-        * [2.2. 流形学习](docs/master/21.md)
-        * [2.3. 聚类](docs/master/22.md)
-        * [2.4. 双聚类](docs/master/23.md)
-        * [2.5. 分解成分中的信号（矩阵分解问题）](docs/master/24.md)
-        * [2.6. 协方差估计](docs/master/25.md)
-        * [2.7. 新奇和异常值检测](docs/master/26.md)
-        * [2.8. 密度估计](docs/master/27.md)
-        * [2.9. 神经网络模型（无监督）](docs/master/28.md)
-    * [3. 模型选择和评估](docs/master/29.md)
-        * [3.1. 交叉验证：评估估算器的表现](docs/master/30.md)
-        * [3.2. 调整估计器的超参数](docs/master/31.md)
-        * [3.3. 模型评估: 量化预测的质量](docs/master/32.md)
-        * [3.4. 模型持久化](docs/master/33.md)
-        * [3.5. 验证曲线: 绘制分数以评估模型](docs/master/34.md)
-    * [4.  检验](docs/master/35.md)
-        * [4.1. 部分依赖图](docs/master/36.md)
-    * [5. 数据集转换](docs/master/37.md)
-        * [5.1. Pipeline（管道）和 FeatureUnion（特征联合）: 合并的评估器](docs/master/38.md)
-        * [5.2. 特征提取](docs/master/39.md)
-        * [5.3 预处理数据](docs/master/40.md)
-        * [5.4 缺失值插补](docs/master/41.md)
-        * [5.5. 无监督降维](docs/master/42.md)
-        * [5.6. 随机投影](docs/master/43.md)
-        * [5.7. 内核近似](docs/master/44.md)
-        * [5.8. 成对的矩阵, 类别和核函数](docs/master/45.md)
-        * [5.9. 预测目标 (`y`) 的转换](docs/master/46.md)
-    * [6. 数据集加载工具](docs/master/47.md)
-        * [6.1. 通用数据集 API](docs/master/47.md)
-        * [6.2. 玩具数据集](docs/master/47.md)
-        * [6.3 真实世界中的数据集](docs/master/47.md)
-        * [6.4. 样本生成器](docs/master/47.md)
-        * [6.5. 加载其他数据集](docs/master/47.md)
-    * [7. 使用scikit-learn计算](docs/master/48.md)
-        * [7.1. 大规模计算的策略: 更大量的数据](docs/master/48.md)
-        * [7.2. 计算性能](docs/master/48.md)
-        * [7.3. 并行性、资源管理和配置](docs/master/48.md)
-*   [教程](docs/master/50.md)
-    *   [使用 scikit-learn 介绍机器学习](docs/master/51.md)
-    *   [关于科学数据处理的统计学习教程](docs/master/52.md)
-        *   [机器学习: scikit-learn 中的设置以及预估对象](docs/master/53.md)
-        *   [监督学习：从高维观察预测输出变量](docs/master/54.md)
-        *   [模型选择：选择估计量及其参数](docs/master/55.md)
-        *   [无监督学习: 寻求数据表示](docs/master/56.md)
-        *   [把它们放在一起](docs/master/57.md)
-        *   [寻求帮助](docs/master/58.md)
-    *   [处理文本数据](docs/master/59.md)
-    *   [选择正确的评估器(estimator.md)](docs/master/60.md)
-    *   [外部资源，视频和谈话](docs/master/61.md)
-*   [API 参考](https://scikit-learn.org/stable/modules/classes.html)
-*   [常见问题](docs/master/63.md)
-*   [时光轴](docs/master/64.md)

Python/sklearn/sklearn-doc-zh/examples/Biclustering/a_demo_of_the_spectral_clustering_algorithm.md

@@ -1,78 +0,0 @@
-
-# 频谱双聚类算法演示
-
-> 翻译者：[@N!no](https://github.com/lovelybuggies) 
-> 校验者：待校验
-
-这个例子演示了如何使用光谱聚类算法生成棋盘数据集并对其进行聚类处理。
-
-数据是用`make_checkerboard`函数生成的，然后打乱顺序并传递给光谱双聚类算法。变换后的矩阵的行和列被重新排列，以显示该算法找到的双聚类。
-
-行和列标签向量的外积表示棋盘结构。
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_001.png)
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_002.png)
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_003.png)
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_biclustering_004.png)
-
-```
-consensus score: 1.0
-```
-
-
-```python
-print(__doc__)
-
-# Author: Kemal Eren <kemal@kemaleren.com>
-# License: BSD 3 clause
-
-import numpy as np
-from matplotlib import pyplot as plt
-
-from sklearn.datasets import make_checkerboard
-from sklearn.cluster import SpectralBiclustering
-from sklearn.metrics import consensus_score
-
-
-n_clusters = (4, 3)
-data, rows, columns = make_checkerboard(
-    shape=(300, 300), n_clusters=n_clusters, noise=10,
-    shuffle=False, random_state=0)
-
-plt.matshow(data, cmap=plt.cm.Blues)
-plt.title("Original dataset")
-
-# 打乱聚类顺序
-rng = np.random.RandomState(0)
-row_idx = rng.permutation(data.shape[0])
-col_idx = rng.permutation(data.shape[1])
-data = data[row_idx][:, col_idx]
-
-plt.matshow(data, cmap=plt.cm.Blues)
-plt.title("Shuffled dataset")
-
-model = SpectralBiclustering(n_clusters=n_clusters, method='log',
-                             random_state=0)
-model.fit(data)
-score = consensus_score(model.biclusters_,
-                        (rows[:, row_idx], columns[:, col_idx]))
-
-print("consensus score: {:.1f}".format(score))
-
-fit_data = data[np.argsort(model.row_labels_)]
-fit_data = fit_data[:, np.argsort(model.column_labels_)]
-
-plt.matshow(fit_data, cmap=plt.cm.Blues)
-plt.title("After biclustering; rearranged to show biclusters")
-
-plt.matshow(np.outer(np.sort(model.row_labels_) + 1,
-                     np.sort(model.column_labels_) + 1),
-            cmap=plt.cm.Blues)
-plt.title("Checkerboard structure of rearranged data")
-
-plt.show()
-```
-

Python/sklearn/sklearn-doc-zh/examples/Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md

@@ -1,66 +0,0 @@
-
-# 频谱共聚算法演示
-
-> 翻译者：[@N!no](https://github.com/lovelybuggies) 
-> 校验者：待校验
-
-这个例子演示了如何使用谱协聚类算法生成数据集并对其进行双聚类处理。
-
-数据集是使用 `make_biclusters` 函数生成的，该函数创建一个小值矩阵，并将大值植入双聚类。然后将行和列打乱并传递给光谱协聚算法。通过重新排列变换后的矩阵可以使双聚类连续，这展示出该算法找到双聚类的准确性。
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_coclustering_001.png)
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_coclustering_002.png)
-
-![png](https://scikit-learn.org/stable/_images/sphx_glr_plot_spectral_coclustering_003.png)
-
-```
-consensus score: 1.0
-```
-
-
-```python
-print(__doc__)
-
-# Author: Kemal Eren <kemal@kemaleren.com>
-# License: BSD 3 clause
-
-import numpy as np
-from matplotlib import pyplot as plt
-
-from sklearn.datasets import make_biclusters
-from sklearn.cluster import SpectralCoclustering
-from sklearn.metrics import consensus_score
-
-data, rows, columns = make_biclusters(
-    shape=(300, 300), n_clusters=5, noise=5,
-    shuffle=False, random_state=0)
-
-plt.matshow(data, cmap=plt.cm.Blues)
-plt.title("Original dataset")
-
-# 打乱聚类的位置
-rng = np.random.RandomState(0)
-row_idx = rng.permutation(data.shape[0])
-col_idx = rng.permutation(data.shape[1])
-data = data[row_idx][:, col_idx]
-
-plt.matshow(data, cmap=plt.cm.Blues)
-plt.title("Shuffled dataset")
-
-model = SpectralCoclustering(n_clusters=5, random_state=0)
-model.fit(data)
-score = consensus_score(model.biclusters_,
-                        (rows[:, row_idx], columns[:, col_idx]))
-
-print("consensus score: {:.3f}".format(score))
-
-fit_data = data[np.argsort(model.row_labels_)]
-fit_data = fit_data[:, np.argsort(model.column_labels_)]
-
-plt.matshow(fit_data, cmap=plt.cm.Blues)
-plt.title("After biclustering; rearranged to show biclusters")
-
-plt.show()
-```
-

Python/sklearn/sklearn-doc-zh/examples/Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md

@@ -1,174 +0,0 @@
-
-# 使用频谱共聚算法对文档进行聚合
-
-> 翻译者：[@N!no](https://github.com/lovelybuggies) 
-> 校验者：待校验
-
-这个例子演示了20个新闻组数据集上的光谱协聚类算法。‘comp.os.ms-windows.misc’ 类别被排除在外，因为它包含许多只包含数据的帖子。
-
-TF-IDF 矢量帖构成一个词频矩阵，然后使用 Dhillon 光谱协聚算法对其进行重组。由此产生的文档词双聚类表明在这些子集文档中被使用频率更高的子集词。
-
-对于一些最好的双聚类来说，它最常见的文档类别和十个最重要的单词会被打印出来。最佳双类别由其归一化的切割决定。最好的单词是通过比较它们在两区内和两区外的总和来确定的。
-
-为了进行比较，我们还使用 MiniBatchKMeans 对文档进行集群。从双聚类衍生出的文档聚类比使用 MiniBatchKMeans 得到的聚类具有更好的 V-measure。
-
-```
-Vectorizing...
-Coclustering...
-Done in 2.75s. V-measure: 0.4387
-MiniBatchKMeans...
-Done in 5.69s. V-measure: 0.3344
-
-Best biclusters:
-----------------
-bicluster 0 : 1829 documents, 2524 words
-categories   : 22% comp.sys.ibm.pc.hardware, 19% comp.sys.mac.hardware, 18% comp.graphics
-words        : card, pc, ram, drive, bus, mac, motherboard, port, windows, floppy
-
-bicluster 1 : 2391 documents, 3275 words
-categories   : 18% rec.motorcycles, 17% rec.autos, 15% sci.electronics
-words        : bike, engine, car, dod, bmw, honda, oil, motorcycle, behanna, ysu
-
-bicluster 2 : 1887 documents, 4232 words
-categories   : 23% talk.politics.guns, 19% talk.politics.misc, 13% sci.med
-words        : gun, guns, firearms, geb, drugs, banks, dyer, amendment, clinton, cdt
-
-bicluster 3 : 1146 documents, 3263 words
-categories   : 29% talk.politics.mideast, 26% soc.religion.christian, 25% alt.atheism
-words        : god, jesus, christians, atheists, kent, sin, morality, belief, resurrection, marriage
-
-bicluster 4 : 1732 documents, 3967 words
-categories   : 26% sci.crypt, 23% sci.space, 17% sci.med
-words        : clipper, encryption, key, escrow, nsa, crypto, keys, intercon, secure, wiretap
-```
-
-
-```python
-from collections import defaultdict
-import operator
-from time import time
-
-import numpy as np
-
-from sklearn.cluster import SpectralCoclustering
-from sklearn.cluster import MiniBatchKMeans
-from sklearn.datasets import fetch_20newsgroups
-from sklearn.feature_extraction.text import TfidfVectorizer
-from sklearn.metrics.cluster import v_measure_score
-
-print(__doc__)
-
-
-def number_normalizer(tokens):
-    """ 将所有数字标记映射到占位符。
-
-    对于许多应用程序来说，以数字开头的令牌并没有直接的用处，但是这样的令牌存在的事实可能是相关的。通过应用这种降维形式，一些方法可能会表现得更好。
-    """
-    return ("#NUMBER" if token[0].isdigit() else token for token in tokens)
-
-
-class NumberNormalizingVectorizer(TfidfVectorizer):
-    def build_tokenizer(self):
-        tokenize = super().build_tokenizer()
-        return lambda doc: list(number_normalizer(tokenize(doc)))
-
-
-# 不包含 'comp.os.ms-windows.misc' 类别
-categories = ['alt.atheism', 'comp.graphics',
-              'comp.sys.ibm.pc.hardware', 'comp.sys.mac.hardware',
-              'comp.windows.x', 'misc.forsale', 'rec.autos',
-              'rec.motorcycles', 'rec.sport.baseball',
-              'rec.sport.hockey', 'sci.crypt', 'sci.electronics',
-              'sci.med', 'sci.space', 'soc.religion.christian',
-              'talk.politics.guns', 'talk.politics.mideast',
-              'talk.politics.misc', 'talk.religion.misc']
-newsgroups = fetch_20newsgroups(categories=categories)
-y_true = newsgroups.target
-
-vectorizer = NumberNormalizingVectorizer(stop_words='english', min_df=5)
-cocluster = SpectralCoclustering(n_clusters=len(categories),
-                                 svd_method='arpack', random_state=0)
-kmeans = MiniBatchKMeans(n_clusters=len(categories), batch_size=20000,
-                         random_state=0)
-
-print("Vectorizing...")
-X = vectorizer.fit_transform(newsgroups.data)
-
-print("Coclustering...")
-start_time = time()
-cocluster.fit(X)
-y_cocluster = cocluster.row_labels_
-print("Done in {:.2f}s. V-measure: {:.4f}".format(
-    time() - start_time,
-    v_measure_score(y_cocluster, y_true)))
-
-print("MiniBatchKMeans...")
-start_time = time()
-y_kmeans = kmeans.fit_predict(X)
-print("Done in {:.2f}s. V-measure: {:.4f}".format(
-    time() - start_time,
-    v_measure_score(y_kmeans, y_true)))
-
-feature_names = vectorizer.get_feature_names()
-document_names = list(newsgroups.target_names[i] for i in newsgroups.target)
-
-
-def bicluster_ncut(i):
-    rows, cols = cocluster.get_indices(i)
-    if not (np.any(rows) and np.any(cols)):
-        import sys
-        return sys.float_info.max
-    row_complement = np.nonzero(np.logical_not(cocluster.rows_[i]))[0]
-    col_complement = np.nonzero(np.logical_not(cocluster.columns_[i]))[0]
-    # 注意：接下来的操作等同于 X[rows[:, np.newaxis], cols].sum() 
-    #      但是会针对于 scipy <= 0.16 的版本更快一些
-    weight = X[rows][:, cols].sum()
-    cut = (X[row_complement][:, cols].sum() +
-           X[rows][:, col_complement].sum())
-    return cut / weight
-
-
-def most_common(d):
-    """默认字典有最大值的项。
-
-    在 Python >= 2.7 中类似于 Counter.most_common 。
-    """
-    return sorted(d.items(), key=operator.itemgetter(1), reverse=True)
-
-
-bicluster_ncuts = list(bicluster_ncut(i)
-                       for i in range(len(newsgroups.target_names)))
-best_idx = np.argsort(bicluster_ncuts)[:5]
-
-print()
-print("Best biclusters:")
-print("----------------")
-for idx, cluster in enumerate(best_idx):
-    n_rows, n_cols = cocluster.get_shape(cluster)
-    cluster_docs, cluster_words = cocluster.get_indices(cluster)
-    if not len(cluster_docs) or not len(cluster_words):
-        continue
-
-    # 种类
-    counter = defaultdict(int)
-    for i in cluster_docs:
-        counter[document_names[i]] += 1
-    cat_string = ", ".join("{:.0f}% {}".format(float(c) / n_rows * 100, name)
-                           for name, c in most_common(counter)[:3])
-
-    # 单词
-    out_of_cluster_docs = cocluster.row_labels_ != cluster
-    out_of_cluster_docs = np.where(out_of_cluster_docs)[0]
-    word_col = X[:, cluster_words]
-    word_scores = np.array(word_col[cluster_docs, :].sum(axis=0) -
-                           word_col[out_of_cluster_docs, :].sum(axis=0))
-    word_scores = word_scores.ravel()
-    important_words = list(feature_names[cluster_words[i]]
-                           for i in word_scores.argsort()[:-11:-1])
-
-    print("bicluster {} : {} documents, {} words".format(
-        idx, n_rows, n_cols))
-    print("categories   : {}".format(cat_string))
-    print("words        : {}\n".format(', '.join(important_words)))
-```
-

Python/sklearn/sklearn-doc-zh/examples/README.md

@@ -1,316 +0,0 @@
-# 示例
-
-英文地址: <https://scikit-learn.org/stable/auto_examples/index.html>
-
-## 其他示例
-
-scikit-learn 的 Miscellaneous 和入门示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_changed_only_pprint_parameter_thumb.png) <br/> [紧凑的估计表示](https://scikit-learn.org/stable/auto_examples/plot_changed_only_pprint_parameter.html#sphx-glr-auto-examples-plot-changed-only-pprint-parameter-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id2) | ![](img/sphx_glr_plot_roc_curve_visualization_api_thumb.png) <br/> [带有可视化API的ROC曲线](https://scikit-learn.org/stable/auto_examples/plot_roc_curve_visualization_api.html#sphx-glr-auto-examples-plot-roc-curve-visualization-api-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id3) | ![](img/sphx_glr_plot_isotonic_regression_thumb.png) <br/> [序回归](https://scikit-learn.org/stable/auto_examples/plot_isotonic_regression.html#sphx-glr-auto-examples-plot-isotonic-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id4) | ![](img/sphx_glr_plot_partial_dependence_visualization_api_thumb.png) <br/> [先进的绘图具有部分依赖](https://scikit-learn.org/stable/auto_examples/plot_partial_dependence_visualization_api.html#sphx-glr-auto-examples-plot-partial-dependence-visualization-api-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id5) |
-| ![](img/sphx_glr_plot_multioutput_face_completion_thumb.png) <br/> [使用多输出估计器完成人脸](https://scikit-learn.org/stable/auto_examples/plot_multioutput_face_completion.html#sphx-glr-auto-examples-plot-multioutput-face-completion-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id6) | ![](img/sphx_glr_plot_multilabel_thumb.png) <br/> [多标签分类](https://scikit-learn.org/stable/auto_examples/plot_multilabel.html#sphx-glr-auto-examples-plot-multilabel-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id7) | ![](img/sphx_glr_plot_anomaly_comparison_thumb.png) <br/> [比较异常检测算法以对玩具数据集进行异常检测](https://scikit-learn.org/stable/auto_examples/plot_anomaly_comparison.html#sphx-glr-auto-examples-plot-anomaly-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id8) | ![](img/sphx_glr_plot_johnson_lindenstrauss_bound_thumb.png) <br/> [具有随机投影嵌入的Johnson-Lindenstrauss边界](https://scikit-learn.org/stable/auto_examples/plot_johnson_lindenstrauss_bound.html#sphx-glr-auto-examples-plot-johnson-lindenstrauss-bound-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id9) |
-| ![](img/sphx_glr_plot_kernel_ridge_regression_thumb.png) <br/> [内核岭回归和SVR的比较](https://scikit-learn.org/stable/auto_examples/plot_kernel_ridge_regression.html#sphx-glr-auto-examples-plot-kernel-ridge-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id10) | ![](img/sphx_glr_plot_kernel_approximation_thumb.png) <br/> [RBF内核的显式特征图逼近](https://scikit-learn.org/stable/auto_examples/plot_kernel_approximation.html#sphx-glr-auto-examples-plot-kernel-approximation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id11) |
-
-
-## 双聚类
-
-有关`sklearn.cluster.bicluster`模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_spectral_coclustering_thumb.png) <br/> [频谱共聚算法演示](Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md) | ![](img/sphx_glr_plot_spectral_biclustering_thumb.png) <br/> [频谱双聚类算法的演示](Biclustering/a_demo_of_the_spectral_clustering_algorithm.md) | ![](img/sphx_glr_plot_bicluster_newsgroups_thumb.png) <br/> [使用频谱共聚算法对文档进行聚合](Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md) ||
-
-
-## 校准
-
-举例说明了对分类器的预测概率进行校准的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_compare_calibration_thumb.png) <br/> [分类器校准的比较](https://scikit-learn.org/stable/auto_examples/calibration/plot_compare_calibration.html#sphx-glr-auto-examples-calibration-plot-compare-calibration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id15) | ![](img/sphx_glr_plot_calibration_curve_thumb.png) <br/> [概率校准曲线](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_curve.html#sphx-glr-auto-examples-calibration-plot-calibration-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id16) | ![](img/sphx_glr_plot_calibration_thumb.png) <br/> [分类器的概率校准](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration.html#sphx-glr-auto-examples-calibration-plot-calibration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id17) | ![](img/sphx_glr_plot_calibration_multiclass_thumb.png) <br/> [3级分类的概率校准](https://scikit-learn.org/stable/auto_examples/calibration/plot_calibration_multiclass.html#sphx-glr-auto-examples-calibration-plot-calibration-multiclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id18) |
-
-
-## 分类
-
-有关分类算法的一般示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_lda_thumb.png) <br/> [分类法线和收缩线线性判别分析](https://scikit-learn.org/stable/auto_examples/classification/plot_lda.html#sphx-glr-auto-examples-classification-plot-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id19) | ![](img/sphx_glr_plot_digits_classification_thumb.png) <br/> [识别手写数字](https://scikit-learn.org/stable/auto_examples/classification/plot_digits_classification.html#sphx-glr-auto-examples-classification-plot-digits-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id20) | ![](img/sphx_glr_plot_classification_probability_thumb.png) <br/> [情节分类概率](https://scikit-learn.org/stable/auto_examples/classification/plot_classification_probability.html#sphx-glr-auto-examples-classification-plot-classification-probability-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id21) | ![](img/sphx_glr_plot_classifier_comparison_thumb.png) <br/> [分类器比较](https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html#sphx-glr-auto-examples-classification-plot-classifier-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id22) |
-| ![](img/sphx_glr_plot_lda_qda_thumb.png) <br/> [线性和二次判别分析与协方差椭球](https://scikit-learn.org/stable/auto_examples/classification/plot_lda_qda.html#sphx-glr-auto-examples-classification-plot-lda-qda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id23) |
-
-
-## 多聚类
-
-有关[`sklearn.cluster`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cluster "sklearn.cluster")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_agglomerative_dendrogram_thumb.png) <br/> [绘制层次聚类树状图](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_dendrogram.html#sphx-glr-auto-examples-cluster-plot-agglomerative-dendrogram-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id24) | ![](img/sphx_glr_plot_digits_agglomeration_thumb.png) <br/> [功能集聚](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_agglomeration.html#sphx-glr-auto-examples-cluster-plot-digits-agglomeration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id25) | ![](img/sphx_glr_plot_mean_shift_thumb.png) <br/> [均值漂移聚类算法的演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_mean_shift.html#sphx-glr-auto-examples-cluster-plot-mean-shift-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id26) | ![](img/sphx_glr_plot_kmeans_assumptions_thumb.png) <br/> [的k均值假设示范](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_assumptions.html#sphx-glr-auto-examples-cluster-plot-kmeans-assumptions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id27) |
-| ![](img/sphx_glr_plot_dict_face_patches_thumb.png) <br/> [在线学习面部表情字典](https://scikit-learn.org/stable/auto_examples/cluster/plot_dict_face_patches.html#sphx-glr-auto-examples-cluster-plot-dict-face-patches-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id28) | ![](img/sphx_glr_plot_face_compress_thumb.png) <br/> [矢量量化示例](https://scikit-learn.org/stable/auto_examples/cluster/plot_face_compress.html#sphx-glr-auto-examples-cluster-plot-face-compress-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id29) | ![](img/sphx_glr_plot_affinity_propagation_thumb.png) <br/> [相似性传播聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_affinity_propagation.html#sphx-glr-auto-examples-cluster-plot-affinity-propagation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id30) | ![](img/sphx_glr_plot_agglomerative_clustering_thumb.png) <br/> [有和没有结构的聚集聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id31) |
-| ![](img/sphx_glr_plot_coin_segmentation_thumb.png) <br/> [分割区域中希腊硬币的图片](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-segmentation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id32) | ![](img/sphx_glr_plot_digits_linkage_thumb.png) <br/> [二维数字嵌入中的各种聚集聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_digits_linkage.html#sphx-glr-auto-examples-cluster-plot-digits-linkage-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id33) | ![](img/sphx_glr_plot_cluster_iris_thumb.png) <br/> [K-means聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_iris.html#sphx-glr-auto-examples-cluster-plot-cluster-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id34) | ![](img/sphx_glr_plot_segmentation_toy_thumb.png) <br/> [光谱聚类用于图像分割](https://scikit-learn.org/stable/auto_examples/cluster/plot_segmentation_toy.html#sphx-glr-auto-examples-cluster-plot-segmentation-toy-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id35) |
-| ![](img/sphx_glr_plot_coin_ward_segmentation_thumb.png) <br/> [硬币图像上的结构化Ward层次聚类演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_coin_ward_segmentation.html#sphx-glr-auto-examples-cluster-plot-coin-ward-segmentation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id36) | ![](img/sphx_glr_plot_dbscan_thumb.png) <br/> [DBSCAN聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_dbscan.html#sphx-glr-auto-examples-cluster-plot-dbscan-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id37) | ![](img/sphx_glr_plot_color_quantization_thumb.png) <br/> [使用K均值的颜色量化](https://scikit-learn.org/stable/auto_examples/cluster/plot_color_quantization.html#sphx-glr-auto-examples-cluster-plot-color-quantization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id38) | ![](img/sphx_glr_plot_ward_structured_vs_unstructured_thumb.png) <br/> [分层聚类：结构化与非结构化病房](https://scikit-learn.org/stable/auto_examples/cluster/plot_ward_structured_vs_unstructured.html#sphx-glr-auto-examples-cluster-plot-ward-structured-vs-unstructured-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id39) |
-| ![](img/sphx_glr_plot_agglomerative_clustering_metrics_thumb.png) <br/> [具有不同指标的聚集集群](https://scikit-learn.org/stable/auto_examples/cluster/plot_agglomerative_clustering_metrics.html#sphx-glr-auto-examples-cluster-plot-agglomerative-clustering-metrics-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id40) | ![](img/sphx_glr_plot_inductive_clustering_thumb.png) <br/> [归纳聚类](https://scikit-learn.org/stable/auto_examples/cluster/plot_inductive_clustering.html#sphx-glr-auto-examples-cluster-plot-inductive-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id41) | ![](img/sphx_glr_plot_optics_thumb.png) <br/> [OPTICS聚类算法演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_optics.html#sphx-glr-auto-examples-cluster-plot-optics-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id42) | ![](img/sphx_glr_plot_birch_vs_minibatchkmeans_thumb.png) <br/> [比较桦木和MiniBatchKMeans](https://scikit-learn.org/stable/auto_examples/cluster/plot_birch_vs_minibatchkmeans.html#sphx-glr-auto-examples-cluster-plot-birch-vs-minibatchkmeans-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id43) |
-| ![](img/sphx_glr_plot_kmeans_stability_low_dim_dense_thumb.png) <br/> [k均值初始化影响的实证评估](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_stability_low_dim_dense.html#sphx-glr-auto-examples-cluster-plot-kmeans-stability-low-dim-dense-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id44) | ![](img/sphx_glr_plot_adjusted_for_chance_measures_thumb.png) <br/> [集群绩效评估中机会的调整](https://scikit-learn.org/stable/auto_examples/cluster/plot_adjusted_for_chance_measures.html#sphx-glr-auto-examples-cluster-plot-adjusted-for-chance-measures-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id45) | ![](img/sphx_glr_plot_mini_batch_kmeans_thumb.png) <br/> [K-Means和MiniBatchKMeans聚类算法的比较](https://scikit-learn.org/stable/auto_examples/cluster/plot_mini_batch_kmeans.html#sphx-glr-auto-examples-cluster-plot-mini-batch-kmeans-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id46) | ![](img/sphx_glr_plot_feature_agglomeration_vs_univariate_selection_thumb.png) <br/> [特征集聚与单变量选择](https://scikit-learn.org/stable/auto_examples/cluster/plot_feature_agglomeration_vs_univariate_selection.html#sphx-glr-auto-examples-cluster-plot-feature-agglomeration-vs-univariate-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id47) |
-| ![](img/sphx_glr_plot_kmeans_digits_thumb.png) <br/> [手写数字数据上的K-Means聚类演示](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_digits.html#sphx-glr-auto-examples-cluster-plot-kmeans-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id48) | ![](img/sphx_glr_plot_linkage_comparison_thumb.png) <br/> [比较玩具数据集上的不同层次链接方法](https://scikit-learn.org/stable/auto_examples/cluster/plot_linkage_comparison.html#sphx-glr-auto-examples-cluster-plot-linkage-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id49) | ![](img/sphx_glr_plot_kmeans_silhouette_analysis_thumb.png) <br/> [在KMeans聚类上通过轮廓分析选择聚类数量](https://scikit-learn.org/stable/auto_examples/cluster/plot_kmeans_silhouette_analysis.html#sphx-glr-auto-examples-cluster-plot-kmeans-silhouette-analysis-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id50) | ![](img/sphx_glr_plot_cluster_comparison_thumb.png) <br/> [比较玩具数据集上的不同聚类算法](https://scikit-learn.org/stable/auto_examples/cluster/plot_cluster_comparison.html#sphx-glr-auto-examples-cluster-plot-cluster-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id51) |
-
-
-## 协方差估计
-
-有关[`sklearn.covariance`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.covariance)模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_lw_vs_oas_thumb.png) <br/> [Ledoit-Wolf与OAS估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_lw_vs_oas.html#sphx-glr-auto-examples-covariance-plot-lw-vs-oas-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id52) | ![](img/sphx_glr_plot_sparse_cov_thumb.png) <br/> [稀疏逆协方差估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_sparse_cov.html#sphx-glr-auto-examples-covariance-plot-sparse-cov-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id53) | ![](img/sphx_glr_plot_covariance_estimation_thumb.png) <br/> [收缩协方差估计：LedoitWolf与OAS和最大似然性](https://scikit-learn.org/stable/auto_examples/covariance/plot_covariance_estimation.html#sphx-glr-auto-examples-covariance-plot-covariance-estimation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id54) | ![](img/sphx_glr_plot_mahalanobis_distances_thumb.png) <br/> [健壮的协方差估计和马氏距离相关性](https://scikit-learn.org/stable/auto_examples/covariance/plot_mahalanobis_distances.html#sphx-glr-auto-examples-covariance-plot-mahalanobis-distances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id55) |
-| ![](img/sphx_glr_plot_robust_vs_empirical_covariance_thumb.png) <br/> [乐百氏VS实证协方差估计](https://scikit-learn.org/stable/auto_examples/covariance/plot_robust_vs_empirical_covariance.html#sphx-glr-auto-examples-covariance-plot-robust-vs-empirical-covariance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id56) |
-
-
-## 交叉分解
-
-有关[`sklearn.cross_decomposition`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.cross_decomposition "sklearn.cross_decomposition")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_compare_cross_decomposition_thumb.png) <br/> [比较交叉分解方法](https://scikit-learn.org/stable/auto_examples/cross_decomposition/plot_compare_cross_decomposition.html#sphx-glr-auto-examples-cross-decomposition-plot-compare-cross-decomposition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id57) |
-
-
-## 数据集示例
-
-有关[`sklearn.datasets`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.datasets "sklearn.datasets")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_digits_last_image_thumb.png) <br/> [Digit数据集](https://scikit-learn.org/stable/auto_examples/datasets/plot_digits_last_image.html#sphx-glr-auto-examples-datasets-plot-digits-last-image-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id58) | ![](img/sphx_glr_plot_iris_dataset_thumb.png) <br/> [虹膜数据集](https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html#sphx-glr-auto-examples-datasets-plot-iris-dataset-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id59) | ![](img/sphx_glr_plot_random_dataset_thumb.png) <br/> [绘制随机生成的分类数据集](https://scikit-learn.org/stable/auto_examples/datasets/plot_random_dataset.html#sphx-glr-auto-examples-datasets-plot-random-dataset-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id60) | ![](img/sphx_glr_plot_random_multilabel_dataset_thumb.png) <br/> [绘制随机生成的多标签数据集](https://scikit-learn.org/stable/auto_examples/datasets/plot_random_multilabel_dataset.html#sphx-glr-auto-examples-datasets-plot-random-multilabel-dataset-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id61) |
-
-
-## 决策树
-
-有关[`sklearn.tree`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.tree "sklearn.tree")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_tree_regression_thumb.png) <br/> [决策树回归](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression.html#sphx-glr-auto-examples-tree-plot-tree-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id62) | ![](img/sphx_glr_plot_tree_regression_multioutput_thumb.png) <br/> [多路输出决策树回归](https://scikit-learn.org/stable/auto_examples/tree/plot_tree_regression_multioutput.html#sphx-glr-auto-examples-tree-plot-tree-regression-multioutput-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id63) | ![](img/sphx_glr_plot_iris_dtc_thumb.png) <br/> [在虹膜数据集上绘制决策树的决策面](https://scikit-learn.org/stable/auto_examples/tree/plot_iris_dtc.html#sphx-glr-auto-examples-tree-plot-iris-dtc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id64) | ![](img/sphx_glr_plot_cost_complexity_pruning_thumb.png) <br/> [使用成本复杂度修剪来修剪修剪决策树](https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id65) |
-| ![](img/sphx_glr_plot_unveil_tree_structure_thumb.png) <br/> [了解决策树结构](https://scikit-learn.org/stable/auto_examples/tree/plot_unveil_tree_structure.html#sphx-glr-auto-examples-tree-plot-unveil-tree-structure-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id66) |
-
-
-## 分解
-
-有关[`sklearn.decomposition`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.decomposition "sklearn。分解")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_beta_divergence_thumb.png) <br/> [β-发散损失函数](https://scikit-learn.org/stable/auto_examples/decomposition/plot_beta_divergence.html#sphx-glr-auto-examples-decomposition-plot-beta-divergence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id67) | ![](img/sphx_glr_plot_pca_iris_thumb.png) <br/> [具有虹膜数据集的PCA示例](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_iris.html#sphx-glr-auto-examples-decomposition-plot-pca-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id68) | ![](img/sphx_glr_plot_incremental_pca_thumb.png) <br/> [增量](https://scikit-learn.org/stable/auto_examples/decomposition/plot_incremental_pca.html#sphx-glr-auto-examples-decomposition-plot-incremental-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id69) | ![](img/sphx_glr_plot_pca_vs_lda_thumb.png) <br/> [Iris数据集的LDA和PCA二维投影的比较](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_lda.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id70) |
-| ![](img/sphx_glr_plot_ica_blind_source_separation_thumb.png) <br/> [使用FastICA进行盲源分离](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_blind_source_separation.html#sphx-glr-auto-examples-decomposition-plot-ica-blind-source-separation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id71) | ![](img/sphx_glr_plot_pca_3d_thumb.png) <br/> [主成分分析（PCA）](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_3d.html#sphx-glr-auto-examples-decomposition-plot-pca-3d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id72) | ![](img/sphx_glr_plot_ica_vs_pca_thumb.png) <br/> [2D点云上的](https://scikit-learn.org/stable/auto_examples/decomposition/plot_ica_vs_pca.html#sphx-glr-auto-examples-decomposition-plot-ica-vs-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id73) | ![](img/sphx_glr_plot_kernel_pca_thumb.png) <br/> [内核](https://scikit-learn.org/stable/auto_examples/decomposition/plot_kernel_pca.html#sphx-glr-auto-examples-decomposition-plot-kernel-pca-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id74) |
-| ![](img/sphx_glr_plot_pca_vs_fa_model_selection_thumb.png) <br/> [概率PCA和因子分析（FA）进行模型选择](https://scikit-learn.org/stable/auto_examples/decomposition/plot_pca_vs_fa_model_selection.html#sphx-glr-auto-examples-decomposition-plot-pca-vs-fa-model-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id75) | ![](img/sphx_glr_plot_sparse_coding_thumb.png) <br/> [使用预先计算的字典进行稀疏编码](https://scikit-learn.org/stable/auto_examples/decomposition/plot_sparse_coding.html#sphx-glr-auto-examples-decomposition-plot-sparse-coding-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id76) | ![](img/sphx_glr_plot_image_denoising_thumb.png) <br/> [图片使用字典学习去噪](https://scikit-learn.org/stable/auto_examples/decomposition/plot_image_denoising.html#sphx-glr-auto-examples-decomposition-plot-image-denoising-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id77) | ![](img/sphx_glr_plot_faces_decomposition_thumb.png) <br/> [Faces数据集分解](https://scikit-learn.org/stable/auto_examples/decomposition/plot_faces_decomposition.html#sphx-glr-auto-examples-decomposition-plot-faces-decomposition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id78) |
-
-
-## 集成方法
-
-有关[`sklearn.ensemble`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.ensemble "sklearn.ensemble")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_forest_importances_faces_thumb.png) <br/> [并行树木森林的像素重要性](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances_faces.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-faces-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id79) | ![](img/sphx_glr_plot_adaboost_regression_thumb.png) <br/> [使用AdaBoost进行决策树回归](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_regression.html#sphx-glr-auto-examples-ensemble-plot-adaboost-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id80) | ![](img/sphx_glr_plot_voting_regressor_thumb.png) <br/> [绘制个人和投票回归预测](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_regressor.html#sphx-glr-auto-examples-ensemble-plot-voting-regressor-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id81) | ![](img/sphx_glr_plot_forest_importances_thumb.png) <br/> [树木森林的功能重要性](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_importances.html#sphx-glr-auto-examples-ensemble-plot-forest-importances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id82) |
-| ![](img/sphx_glr_plot_isolation_forest_thumb.png) <br/> [IsolationForest示例](https://scikit-learn.org/stable/auto_examples/ensemble/plot_isolation_forest.html#sphx-glr-auto-examples-ensemble-plot-isolation-forest-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id83) | ![](img/sphx_glr_plot_voting_decision_regions_thumb.png) <br/> [绘制VotingClassifier的决策边界](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_decision_regions.html#sphx-glr-auto-examples-ensemble-plot-voting-decision-regions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id84) | ![](img/sphx_glr_plot_random_forest_regression_multioutput_thumb.png) <br/> [比较随机森林和多输出元估计器](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_regression_multioutput.html#sphx-glr-auto-examples-ensemble-plot-random-forest-regression-multioutput-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id85) | ![](img/sphx_glr_plot_gradient_boosting_quantile_thumb.png) <br/> [梯度提升回归的预测间隔](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_quantile.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-quantile-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id86) |
-| ![](img/sphx_glr_plot_gradient_boosting_regularization_thumb.png) <br/> [梯度提升正则化](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regularization.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regularization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id87) | ![](img/sphx_glr_plot_voting_probas_thumb.png) <br/> [绘制由VotingClassifier计算的类概率](https://scikit-learn.org/stable/auto_examples/ensemble/plot_voting_probas.html#sphx-glr-auto-examples-ensemble-plot-voting-probas-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id88) | ![](img/sphx_glr_plot_gradient_boosting_regression_thumb.png) <br/> [梯度推进回归](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_regression.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id89) | ![](img/sphx_glr_plot_ensemble_oob_thumb.png) <br/> [随机森林的OOB错误](https://scikit-learn.org/stable/auto_examples/ensemble/plot_ensemble_oob.html#sphx-glr-auto-examples-ensemble-plot-ensemble-oob-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id90) |
-| ![](img/sphx_glr_plot_adaboost_twoclass_thumb.png) <br/> [两个级的AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_twoclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-twoclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id91) | ![](img/sphx_glr_plot_random_forest_embedding_thumb.png) <br/> [使用完全随机树的哈希特征转换](https://scikit-learn.org/stable/auto_examples/ensemble/plot_random_forest_embedding.html#sphx-glr-auto-examples-ensemble-plot-random-forest-embedding-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id92) | ![](img/sphx_glr_plot_adaboost_multiclass_thumb.png) <br/> [多类AdaBoosted决策树](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_multiclass.html#sphx-glr-auto-examples-ensemble-plot-adaboost-multiclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id93) | ![](img/sphx_glr_plot_adaboost_hastie_10_2_thumb.png) <br/> [离散相对真正的AdaBoost](https://scikit-learn.org/stable/auto_examples/ensemble/plot_adaboost_hastie_10_2.html#sphx-glr-auto-examples-ensemble-plot-adaboost-hastie-10-2-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id94) |
-| ![](img/sphx_glr_plot_stack_predictors_thumb.png) <br/> [使用堆叠组合预测](https://scikit-learn.org/stable/auto_examples/ensemble/plot_stack_predictors.html#sphx-glr-auto-examples-ensemble-plot-stack-predictors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id95) | ![](img/sphx_glr_plot_gradient_boosting_early_stopping_thumb.png) <br/> [提前终止的梯度推进](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_early_stopping.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-early-stopping-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id96) | ![](img/sphx_glr_plot_feature_transformation_thumb.png) <br/> [带有树群的特征变换](https://scikit-learn.org/stable/auto_examples/ensemble/plot_feature_transformation.html#sphx-glr-auto-examples-ensemble-plot-feature-transformation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id97) | ![](img/sphx_glr_plot_gradient_boosting_oob_thumb.png) <br/> [梯度提升袋外估计](https://scikit-learn.org/stable/auto_examples/ensemble/plot_gradient_boosting_oob.html#sphx-glr-auto-examples-ensemble-plot-gradient-boosting-oob-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id98) |
-| ![](img/sphx_glr_plot_bias_variance_thumb.png) <br/> [单一估计器与装袋：偏差方差分解](https://scikit-learn.org/stable/auto_examples/ensemble/plot_bias_variance.html#sphx-glr-auto-examples-ensemble-plot-bias-variance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id99) | ![](img/sphx_glr_plot_forest_iris_thumb.png) <br/> [在虹膜数据集上绘制树木合奏的决策面](https://scikit-learn.org/stable/auto_examples/ensemble/plot_forest_iris.html#sphx-glr-auto-examples-ensemble-plot-forest-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id100) |
-
-
-## 基于真实数据集的示例
-
-具有一些中等大小的数据集或交互式用户界面的现实问题的应用程序。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_outlier_detection_housing_thumb.png) <br/> [真实数据集的异常值检测](https://scikit-learn.org/stable/auto_examples/applications/plot_outlier_detection_housing.html#sphx-glr-auto-examples-applications-plot-outlier-detection-housing-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id101) | ![](img/sphx_glr_plot_tomography_l1_reconstruction_thumb.png) <br/> [压缩感测：使用L1先验（Lasso）进行层析成像重建](https://scikit-learn.org/stable/auto_examples/applications/plot_tomography_l1_reconstruction.html#sphx-glr-auto-examples-applications-plot-tomography-l1-reconstruction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id102) | ![](img/sphx_glr_plot_topics_extraction_with_nmf_lda_thumb.png) <br/> [非负矩阵分解和隐含狄利克雷分布话题提取](https://scikit-learn.org/stable/auto_examples/applications/plot_topics_extraction_with_nmf_lda.html#sphx-glr-auto-examples-applications-plot-topics-extraction-with-nmf-lda-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id103) | ![](img/sphx_glr_plot_face_recognition_thumb.png) <br/> [使用特征脸和支持向量机的](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id104 "Permalink to this image")[识别示例](https://scikit-learn.org/stable/auto_examples/applications/plot_face_recognition.html#sphx-glr-auto-examples-applications-plot-face-recognition-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id104) |
-| ![](img/sphx_glr_plot_model_complexity_influence_thumb.png) <br/> [模型复杂度影响](https://scikit-learn.org/stable/auto_examples/applications/plot_model_complexity_influence.html#sphx-glr-auto-examples-applications-plot-model-complexity-influence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id105) | ![](img/sphx_glr_plot_stock_market_thumb.png) <br/> [可视化的股市结构](https://scikit-learn.org/stable/auto_examples/applications/plot_stock_market.html#sphx-glr-auto-examples-applications-plot-stock-market-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id106) | ![](img/sphx_glr_wikipedia_principal_eigenvector_thumb.png) <br/> [维基百科的主要特征向量](https://scikit-learn.org/stable/auto_examples/applications/wikipedia_principal_eigenvector.html#sphx-glr-auto-examples-applications-wikipedia-principal-eigenvector-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id107) | ![](img/sphx_glr_plot_species_distribution_modeling_thumb.png) <br/> [物种分布建模](https://scikit-learn.org/stable/auto_examples/applications/plot_species_distribution_modeling.html#sphx-glr-auto-examples-applications-plot-species-distribution-modeling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id108) |
-| ![](img/sphx_glr_svm_gui_thumb.png) <br/> [Libsvm](https://scikit-learn.org/stable/auto_examples/applications/svm_gui.html#sphx-glr-auto-examples-applications-svm-gui-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id109) | ![](img/sphx_glr_plot_prediction_latency_thumb.png) <br/> [预测延迟](https://scikit-learn.org/stable/auto_examples/applications/plot_prediction_latency.html#sphx-glr-auto-examples-applications-plot-prediction-latency-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id110) | ![](img/sphx_glr_plot_out_of_core_classification_thumb.png) <br/> [文本文档的核心分类](https://scikit-learn.org/stable/auto_examples/applications/plot_out_of_core_classification.html#sphx-glr-auto-examples-applications-plot-out-of-core-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id111) |
-
-
-## 特征选择
-
-有关[`sklearn.feature_selection`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_selection "sklearn.feature_selection")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_rfe_digits_thumb.png) <br/> [递归特征消除](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_digits.html#sphx-glr-auto-examples-feature-selection-plot-rfe-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id112) | ![](img/sphx_glr_plot_f_test_vs_mi_thumb.png) <br/> [F检验和相互信息的比较](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_f_test_vs_mi.html#sphx-glr-auto-examples-feature-selection-plot-f-test-vs-mi-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id113) | ![](img/sphx_glr_plot_feature_selection_pipeline_thumb.png) <br/> [管道Anova](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection_pipeline.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-pipeline-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id114) | ![](img/sphx_glr_plot_rfe_with_cross_validation_thumb.png) <br/> [通过交叉验证消除递归特征](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_rfe_with_cross_validation.html#sphx-glr-auto-examples-feature-selection-plot-rfe-with-cross-validation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id115) |
-| ![](img/sphx_glr_plot_select_from_model_boston_thumb.png) <br/> [使用SelectFromModel和LassoCV特征选择](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_select_from_model_boston.html#sphx-glr-auto-examples-feature-selection-plot-select-from-model-boston-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id116) | ![](img/sphx_glr_plot_permutation_test_for_classification_thumb.png) <br/> [与排列测试的分类评分的意义](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_permutation_test_for_classification.html#sphx-glr-auto-examples-feature-selection-plot-permutation-test-for-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id117) | ![](img/sphx_glr_plot_feature_selection_thumb.png) <br/> [单变量特征选择](https://scikit-learn.org/stable/auto_examples/feature_selection/plot_feature_selection.html#sphx-glr-auto-examples-feature-selection-plot-feature-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id118) |
-
-
-## 高斯混合模型
-
-有关[`sklearn.mixture`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.mixture "sklearn.mixture")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_gmm_pdf_thumb.png) <br/> [高斯混合的密度估计](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_pdf.html#sphx-glr-auto-examples-mixture-plot-gmm-pdf-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id119) | ![](img/sphx_glr_plot_gmm_thumb.png) <br/> [高斯混合模型椭球](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm.html#sphx-glr-auto-examples-mixture-plot-gmm-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id120) | ![](img/sphx_glr_plot_gmm_selection_thumb.png) <br/> [高斯混合模型选择](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_selection.html#sphx-glr-auto-examples-mixture-plot-gmm-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id121) | ![](img/sphx_glr_plot_gmm_covariances_thumb.png) <br/> [GMM协方差](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_covariances.html#sphx-glr-auto-examples-mixture-plot-gmm-covariances-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id122) |
-| ![](img/sphx_glr_plot_gmm_sin_thumb.png) <br/> [高斯混合模型正弦曲线](https://scikit-learn.org/stable/auto_examples/mixture/plot_gmm_sin.html#sphx-glr-auto-examples-mixture-plot-gmm-sin-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id123) | ![](img/sphx_glr_plot_concentration_prior_thumb.png) <br/> [贝叶斯高斯混合变量的浓度先验类型分析](https://scikit-learn.org/stable/auto_examples/mixture/plot_concentration_prior.html#sphx-glr-auto-examples-mixture-plot-concentration-prior-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id124) |
-
-
-## 高斯机器学习过程
-
-有关[`sklearn.gaussian_process`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.gaussian_process "sklearn.gaussian_process")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_gpc_xor_thumb.png) <br/> [XOR数据集上的高斯过程分类（GPC）的图示](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_xor.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-xor-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id125) | ![](img/sphx_glr_plot_gpc_iris_thumb.png) <br/> [虹膜数据集上的高斯过程分类（GPC）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_iris.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id126) | ![](img/sphx_glr_plot_compare_gpr_krr_thumb.png) <br/> [核岭和高斯过程回归的比较](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_compare_gpr_krr.html#sphx-glr-auto-examples-gaussian-process-plot-compare-gpr-krr-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id127) | ![](img/sphx_glr_plot_gpr_prior_posterior_thumb.png) <br/> [不同内核的先验和后验高斯过程的图示](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_prior_posterior.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-prior-posterior-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id128) |
-| ![](img/sphx_glr_plot_gpc_isoprobability_thumb.png) <br/> [高斯过程分类（GPC）的等概率线](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc_isoprobability.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-isoprobability-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id129) | ![](img/sphx_glr_plot_gpc_thumb.png) <br/> [概率预测的结果与高斯过程分类（GPC）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpc.html#sphx-glr-auto-examples-gaussian-process-plot-gpc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id130) | ![](img/sphx_glr_plot_gpr_noisy_thumb.png) <br/> [具有噪声水平估计的高斯过程回归（GPR）](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id131) | ![](img/sphx_glr_plot_gpr_noisy_targets_thumb.png) <br/> [高斯过程回归：基本入门示例](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-noisy-targets-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id132) |
-| ![](img/sphx_glr_plot_gpr_co2_thumb.png) <br/> [基于Mauna Loa CO2数据的高斯过程回归（GPR）。](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_co2.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-co2-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id133) | ![](img/sphx_glr_plot_gpr_on_structured_data_thumb.png) <br/> [离散数据结构上的高斯过程](https://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_on_structured_data.html#sphx-glr-auto-examples-gaussian-process-plot-gpr-on-structured-data-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id134) |
-
-
-## 广义线性模型
-
-有关[`sklearn.linear_model`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.linear_model "sklearn.linear_model")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_lasso_lars_thumb.png) <br/> [使用LARS的套索路径](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_lars.html#sphx-glr-auto-examples-linear-model-plot-lasso-lars-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id135) | ![](img/sphx_glr_plot_ridge_path_thumb.png) <br/> [绘制岭系数作为正则化的函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_path.html#sphx-glr-auto-examples-linear-model-plot-ridge-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id136) | ![](img/sphx_glr_plot_sgd_separating_hyperplane_thumb.png) <br/> [SGD：最大余量分隔超平面](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_separating_hyperplane.html#sphx-glr-auto-examples-linear-model-plot-sgd-separating-hyperplane-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id137) | ![](img/sphx_glr_plot_sgd_loss_functions_thumb.png) <br/> [SGD：凸损失函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_loss_functions.html#sphx-glr-auto-examples-linear-model-plot-sgd-loss-functions-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id138) |
-| ![](img/sphx_glr_plot_ols_ridge_variance_thumb.png) <br/> [普通最小二乘法和岭回归方差](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_ridge_variance.html#sphx-glr-auto-examples-linear-model-plot-ols-ridge-variance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id139) | ![](img/sphx_glr_plot_ridge_coeffs_thumb.png) <br/> [绘制Ridge系数作为L2正则化的函数](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ridge_coeffs.html#sphx-glr-auto-examples-linear-model-plot-ridge-coeffs-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id140) | ![](img/sphx_glr_plot_sgd_penalties_thumb.png) <br/> [SGD：罚款](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_penalties.html#sphx-glr-auto-examples-linear-model-plot-sgd-penalties-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id141) | ![](img/sphx_glr_plot_polynomial_interpolation_thumb.png) <br/> [多项式插值](https://scikit-learn.org/stable/auto_examples/linear_model/plot_polynomial_interpolation.html#sphx-glr-auto-examples-linear-model-plot-polynomial-interpolation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id142) |
-| ![](img/sphx_glr_plot_logistic_thumb.png) <br/> [物流功能](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic.html#sphx-glr-auto-examples-linear-model-plot-logistic-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id143) | ![](img/sphx_glr_plot_logistic_path_thumb.png) <br/> [L1-Logistic回归的正规化道路](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_path.html#sphx-glr-auto-examples-linear-model-plot-logistic-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id144) | ![](img/sphx_glr_plot_iris_logistic_thumb.png) <br/> [Logistic回归3类分类器](https://scikit-learn.org/stable/auto_examples/linear_model/plot_iris_logistic.html#sphx-glr-auto-examples-linear-model-plot-iris-logistic-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id145) | ![](img/sphx_glr_plot_sgd_weighted_samples_thumb.png) <br/> [SGD：加权样本](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_weighted_samples.html#sphx-glr-auto-examples-linear-model-plot-sgd-weighted-samples-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id146) |
-| ![](img/sphx_glr_plot_ols_thumb.png) <br/> [线性回归示例](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols.html#sphx-glr-auto-examples-linear-model-plot-ols-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id147) | ![](img/sphx_glr_plot_ransac_thumb.png) <br/> [使用RANSAC进行稳健的线性模型估计](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ransac.html#sphx-glr-auto-examples-linear-model-plot-ransac-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id148) | ![](img/sphx_glr_plot_ols_3d_thumb.png) <br/> [稀疏实施例：装修仅设有1和2](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ols_3d.html#sphx-glr-auto-examples-linear-model-plot-ols-3d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id149) | ![](img/sphx_glr_plot_huber_vs_ridge_thumb.png) <br/> [HuberRegressor VS岭集具有较强的异常](https://scikit-learn.org/stable/auto_examples/linear_model/plot_huber_vs_ridge.html#sphx-glr-auto-examples-linear-model-plot-huber-vs-ridge-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id150) |
-| ![](img/sphx_glr_plot_lasso_dense_vs_sparse_data_thumb.png) <br/> [套索上密集和稀疏数据](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_dense_vs_sparse_data.html#sphx-glr-auto-examples-linear-model-plot-lasso-dense-vs-sparse-data-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id151) | ![](img/sphx_glr_plot_sgd_comparison_thumb.png) <br/> [比较各种在线求解器](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_comparison.html#sphx-glr-auto-examples-linear-model-plot-sgd-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id152) | ![](img/sphx_glr_plot_multi_task_lasso_support_thumb.png) <br/> [多任务套索的联合特征选择](https://scikit-learn.org/stable/auto_examples/linear_model/plot_multi_task_lasso_support.html#sphx-glr-auto-examples-linear-model-plot-multi-task-lasso-support-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id153) | ![](img/sphx_glr_plot_sparse_logistic_regression_mnist_thumb.png) <br/> [使用多项式逻辑+ L1的MNIST分类](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_mnist.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-mnist-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id154) | ![](img/sphx_glr_plot_sgd_iris_thumb.png) <br/> [在虹膜数据集上绘制多类](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_iris.html#sphx-glr-auto-examples-linear-model-plot-sgd-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id155) |
-| ![](img/sphx_glr_plot_omp_thumb.png) <br/> [正交匹配追踪](https://scikit-learn.org/stable/auto_examples/linear_model/plot_omp.html#sphx-glr-auto-examples-linear-model-plot-omp-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id156) | ![](img/sphx_glr_plot_lasso_and_elasticnet_thumb.png) <br/> [套索和弹性网用于稀疏信号](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_and_elasticnet.html#sphx-glr-auto-examples-linear-model-plot-lasso-and-elasticnet-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id157) | ![](img/sphx_glr_plot_bayesian_ridge_curvefit_thumb.png) <br/> [贝叶斯岭回归的曲线拟合](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge_curvefit.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-curvefit-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id158) | ![](img/sphx_glr_plot_theilsen_thumb.png) <br/> [Theil-Sen回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_theilsen.html#sphx-glr-auto-examples-linear-model-plot-theilsen-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id159) |
-| ![](img/sphx_glr_plot_logistic_multinomial_thumb.png) <br/> [绘制多项式和一对一静态Logistic回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_multinomial.html#sphx-glr-auto-examples-linear-model-plot-logistic-multinomial-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id160) | ![](img/sphx_glr_plot_robust_fit_thumb.png) <br/> [稳健的线性估计器拟合](https://scikit-learn.org/stable/auto_examples/linear_model/plot_robust_fit.html#sphx-glr-auto-examples-linear-model-plot-robust-fit-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id161) | ![](img/sphx_glr_plot_logistic_l1_l2_sparsity_thumb.png) <br/> [Logistic回归中的L1惩罚和稀疏性](https://scikit-learn.org/stable/auto_examples/linear_model/plot_logistic_l1_l2_sparsity.html#sphx-glr-auto-examples-linear-model-plot-logistic-l1-l2-sparsity-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id162) | ![](img/sphx_glr_plot_lasso_coordinate_descent_path_thumb.png) <br/> [套索和弹性网络](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_coordinate_descent_path.html#sphx-glr-auto-examples-linear-model-plot-lasso-coordinate-descent-path-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id163) |
-| ![](img/sphx_glr_plot_ard_thumb.png) <br/> [自动相关性确定回归（ARD）](https://scikit-learn.org/stable/auto_examples/linear_model/plot_ard.html#sphx-glr-auto-examples-linear-model-plot-ard-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id164) | ![](img/sphx_glr_plot_bayesian_ridge_thumb.png) <br/> [贝叶斯岭回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_bayesian_ridge.html#sphx-glr-auto-examples-linear-model-plot-bayesian-ridge-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id165) | ![](img/sphx_glr_plot_sparse_logistic_regression_20newsgroups_thumb.png) <br/> [20newgroups上的多类稀疏逻辑回归](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sparse_logistic_regression_20newsgroups.html#sphx-glr-auto-examples-linear-model-plot-sparse-logistic-regression-20newsgroups-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id166) | ![](img/sphx_glr_plot_lasso_model_selection_thumb.png) <br/> [套索模型选择：交叉验证/ AIC /](https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html#sphx-glr-auto-examples-linear-model-plot-lasso-model-selection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id167) |
-| ![](img/sphx_glr_plot_sgd_early_stopping_thumb.png) <br/> [早期停止随机梯度下降的](https://scikit-learn.org/stable/auto_examples/linear_model/plot_sgd_early_stopping.html#sphx-glr-auto-examples-linear-model-plot-sgd-early-stopping-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id168) |
-
-
-## 检查
-
-与[`sklearn.inspection`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.inspection "斯克莱恩检查")模块有关的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_permutation_importance_multicollinear_thumb.png) <br/> [具有多重共线性或相关特征的置换重要性](https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance_multicollinear.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-multicollinear-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id169) | ![](img/sphx_glr_plot_permutation_importance_thumb.png) <br/> [排列重要性与随机森林特征重要性（MDI）](https://scikit-learn.org/stable/auto_examples/inspection/plot_permutation_importance.html#sphx-glr-auto-examples-inspection-plot-permutation-importance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id170) | ![](img/sphx_glr_plot_partial_dependence_thumb.png) <br/> [部分依赖图](https://scikit-learn.org/stable/auto_examples/inspection/plot_partial_dependence.html#sphx-glr-auto-examples-inspection-plot-partial-dependence-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id171) |
-
-
-## 流形学习
-
-有关[`sklearn.manifold`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.manifold "sklearn。流形")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_swissroll_thumb.png) <br/> [使用LLE减少瑞士卷](https://scikit-learn.org/stable/auto_examples/manifold/plot_swissroll.html#sphx-glr-auto-examples-manifold-plot-swissroll-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id172) | ![](img/sphx_glr_plot_compare_methods_thumb.png) <br/> [流形学习方法的比较](https://scikit-learn.org/stable/auto_examples/manifold/plot_compare_methods.html#sphx-glr-auto-examples-manifold-plot-compare-methods-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id173) | ![](img/sphx_glr_plot_mds_thumb.png) <br/> [多维缩放](https://scikit-learn.org/stable/auto_examples/manifold/plot_mds.html#sphx-glr-auto-examples-manifold-plot-mds-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id174) | ![](img/sphx_glr_plot_t_sne_perplexity_thumb.png) <br/> [叔SNE：各种困惑值对形状的影响](https://scikit-learn.org/stable/auto_examples/manifold/plot_t_sne_perplexity.html#sphx-glr-auto-examples-manifold-plot-t-sne-perplexity-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id175) |
-| ![](img/sphx_glr_plot_manifold_sphere_thumb.png) <br/> [截断球面上的流形学习方法](https://scikit-learn.org/stable/auto_examples/manifold/plot_manifold_sphere.html#sphx-glr-auto-examples-manifold-plot-manifold-sphere-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id176) | ![](img/sphx_glr_plot_lle_digits_thumb.png) <br/> [手写数字流形学习：局部线性嵌入，Isomap…](https://scikit-learn.org/stable/auto_examples/manifold/plot_lle_digits.html#sphx-glr-auto-examples-manifold-plot-lle-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id177) |
-
-
-## 缺失值插补
-
-有关[`sklearn.impute`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.impute "sklearn.impute")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_iterative_imputer_variants_comparison_thumb.png) <br/> [使用IterativeImputer的变体估算缺失值](https://scikit-learn.org/stable/auto_examples/impute/plot_iterative_imputer_variants_comparison.html#sphx-glr-auto-examples-impute-plot-iterative-imputer-variants-comparison-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id178) | ![](img/sphx_glr_plot_missing_values_thumb.png) <br/> [在构建估算器之前估算缺失值](https://scikit-learn.org/stable/auto_examples/impute/plot_missing_values.html#sphx-glr-auto-examples-impute-plot-missing-values-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id179) |
-
-
-## 选型
-
-与[`sklearn.model_selection`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection "sklearn.model_selection")模块有关的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_cv_predict_thumb.png) <br/> [绘制交叉验证的预测](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_predict.html#sphx-glr-auto-examples-model-selection-plot-cv-predict-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id180) | ![](img/sphx_glr_plot_confusion_matrix_thumb.png) <br/> [混淆矩阵](https://scikit-learn.org/stable/auto_examples/model_selection/plot_confusion_matrix.html#sphx-glr-auto-examples-model-selection-plot-confusion-matrix-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id181) | ![](img/sphx_glr_plot_validation_curve_thumb.png) <br/> [绘图验证曲线](https://scikit-learn.org/stable/auto_examples/model_selection/plot_validation_curve.html#sphx-glr-auto-examples-model-selection-plot-validation-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id182) | ![](img/sphx_glr_plot_underfitting_overfitting_thumb.png) <br/> [拟合不足与拟合过度](https://scikit-learn.org/stable/auto_examples/model_selection/plot_underfitting_overfitting.html#sphx-glr-auto-examples-model-selection-plot-underfitting-overfitting-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id183) |
-| ![](img/sphx_glr_plot_grid_search_digits_thumb.png) <br/> [使用带有交叉验证的网格搜索进行参数估计](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_digits.html#sphx-glr-auto-examples-model-selection-plot-grid-search-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id184) | ![](img/sphx_glr_plot_randomized_search_thumb.png) <br/> [对于比较估计超参数随机搜索和网格搜索](https://scikit-learn.org/stable/auto_examples/model_selection/plot_randomized_search.html#sphx-glr-auto-examples-model-selection-plot-randomized-search-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id185) | ![](img/sphx_glr_plot_train_error_vs_test_error_thumb.png) <br/> [训练错误与测试错误](https://scikit-learn.org/stable/auto_examples/model_selection/plot_train_error_vs_test_error.html#sphx-glr-auto-examples-model-selection-plot-train-error-vs-test-error-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id186) | ![](img/sphx_glr_plot_roc_crossval_thumb.png) <br/> [具有交叉验证的接收器操作特性（ROC）](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc_crossval.html#sphx-glr-auto-examples-model-selection-plot-roc-crossval-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id187) |
-| ![](img/sphx_glr_plot_nested_cross_validation_iris_thumb.png) <br/> [嵌套与非嵌套交叉验证](https://scikit-learn.org/stable/auto_examples/model_selection/plot_nested_cross_validation_iris.html#sphx-glr-auto-examples-model-selection-plot-nested-cross-validation-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id188) | ![](img/sphx_glr_plot_multi_metric_evaluation_thumb.png) <br/> [在cross_val_score和GridSearchCV上进行多指标评估的演示](https://scikit-learn.org/stable/auto_examples/model_selection/plot_multi_metric_evaluation.html#sphx-glr-auto-examples-model-selection-plot-multi-metric-evaluation-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id189) | ![](img/sphx_glr_grid_search_text_feature_extraction_thumb.png) <br/> [用于文本特征提取和评估的示例管道](https://scikit-learn.org/stable/auto_examples/model_selection/grid_search_text_feature_extraction.html#sphx-glr-auto-examples-model-selection-grid-search-text-feature-extraction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id190) | ![](img/sphx_glr_plot_grid_search_refit_callable_thumb.png) <br/> [平衡模型的复杂性和交叉验证的分数](https://scikit-learn.org/stable/auto_examples/model_selection/plot_grid_search_refit_callable.html#sphx-glr-auto-examples-model-selection-plot-grid-search-refit-callable-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id191) |
-| ![](img/sphx_glr_plot_cv_indices_thumb.png) <br/> [在scikit-learn中可视化交叉验证行为](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html#sphx-glr-auto-examples-model-selection-plot-cv-indices-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id192) | ![](img/sphx_glr_plot_roc_thumb.png) <br/> [接收器工作特性（ROC）](https://scikit-learn.org/stable/auto_examples/model_selection/plot_roc.html#sphx-glr-auto-examples-model-selection-plot-roc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id193) | ![](img/sphx_glr_plot_precision_recall_thumb.png) <br/> [精密召回](https://scikit-learn.org/stable/auto_examples/model_selection/plot_precision_recall.html#sphx-glr-auto-examples-model-selection-plot-precision-recall-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id194) | ![](img/sphx_glr_plot_learning_curve_thumb.png) <br/> [绘制学习曲线](https://scikit-learn.org/stable/auto_examples/model_selection/plot_learning_curve.html#sphx-glr-auto-examples-model-selection-plot-learning-curve-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id195) |
-
-
-## 多输出方法
-
-有关[`sklearn.multioutput`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.multioutput "sklearn.multioutput")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_classifier_chain_yeast_thumb.png) <br/> [分类器链](https://scikit-learn.org/stable/auto_examples/multioutput/plot_classifier_chain_yeast.html#sphx-glr-auto-examples-multioutput-plot-classifier-chain-yeast-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id196) |
-
-
-## 最近邻
-
-有关[`sklearn.neighbors`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neighbors "sklearn.neighbors")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_regression_thumb.png) <br/> [最近邻居回归](https://scikit-learn.org/stable/auto_examples/neighbors/plot_regression.html#sphx-glr-auto-examples-neighbors-plot-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id197) | ![](img/sphx_glr_plot_lof_outlier_detection_thumb.png) <br/> [使用局部离群因子（LOF）进行离群检测](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_outlier_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-outlier-detection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id198) | ![](img/sphx_glr_plot_classification_thumb.png) <br/> [最近邻居分类](https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html#sphx-glr-auto-examples-neighbors-plot-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id199) | ![](img/sphx_glr_plot_nearest_centroid_thumb.png) <br/> [最近质心分类](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nearest_centroid.html#sphx-glr-auto-examples-neighbors-plot-nearest-centroid-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id200) |
-| ![](img/sphx_glr_plot_digits_kde_sampling_thumb.png) <br/> [核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_digits_kde_sampling.html#sphx-glr-auto-examples-neighbors-plot-digits-kde-sampling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id201) | ![](img/sphx_glr_plot_caching_nearest_neighbors_thumb.png) <br/> [缓存最近的邻居](https://scikit-learn.org/stable/auto_examples/neighbors/plot_caching_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-plot-caching-nearest-neighbors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id202) | ![](img/sphx_glr_plot_nca_illustration_thumb.png) <br/> [邻域成分分析图](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_illustration.html#sphx-glr-auto-examples-neighbors-plot-nca-illustration-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id203) | ![](img/sphx_glr_plot_lof_novelty_detection_thumb.png) <br/> [具有局部异常值（LOF）的新颖性检测](https://scikit-learn.org/stable/auto_examples/neighbors/plot_lof_novelty_detection.html#sphx-glr-auto-examples-neighbors-plot-lof-novelty-detection-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id204) |
-| ![](img/sphx_glr_plot_nca_classification_thumb.png) <br/> [比较具有和不具有邻域分量分析的最近邻域](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_classification.html#sphx-glr-auto-examples-neighbors-plot-nca-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id205) | ![](img/sphx_glr_plot_nca_dim_reduction_thumb.png) <br/> [使用邻域分量分析进行](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id206)[维](https://scikit-learn.org/stable/auto_examples/neighbors/plot_nca_dim_reduction.html#sphx-glr-auto-examples-neighbors-plot-nca-dim-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id206) | ![](img/sphx_glr_plot_species_kde_thumb.png) <br/> [物种分布的核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_species_kde.html#sphx-glr-auto-examples-neighbors-plot-species-kde-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id207) | ![](img/sphx_glr_plot_kde_1d_thumb.png) <br/> [简单的1D内核密度估计](https://scikit-learn.org/stable/auto_examples/neighbors/plot_kde_1d.html#sphx-glr-auto-examples-neighbors-plot-kde-1d-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id208) |
-| ![](img/sphx_glr_approximate_nearest_neighbors_thumb.png) <br/> [TSNE中的近似最近邻居](https://scikit-learn.org/stable/auto_examples/neighbors/approximate_nearest_neighbors.html#sphx-glr-auto-examples-neighbors-approximate-nearest-neighbors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id209) |
-
-
-## 神经网络
-
-有关[`sklearn.neural_network`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.neural_network "sklearn.neural_network")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_mnist_filters_thumb.png) <br/> [在MNIST上可视化MLP权重](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mnist_filters.html#sphx-glr-auto-examples-neural-networks-plot-mnist-filters-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id210) | ![](img/sphx_glr_plot_rbm_logistic_classification_thumb.png) <br/> [用于数字分类的受限玻尔兹曼机功能](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_rbm_logistic_classification.html#sphx-glr-auto-examples-neural-networks-plot-rbm-logistic-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id211) | ![](img/sphx_glr_plot_mlp_alpha_thumb.png) <br/> [在多层感知变化正规化](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_alpha.html#sphx-glr-auto-examples-neural-networks-plot-mlp-alpha-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id212) | ![](img/sphx_glr_plot_mlp_training_curves_thumb.png) <br/> [比较随机学习策略MLPClassifier](https://scikit-learn.org/stable/auto_examples/neural_networks/plot_mlp_training_curves.html#sphx-glr-auto-examples-neural-networks-plot-mlp-training-curves-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id213) |
-
-
-## 管道和复合估计器
-
-由其他估算器组成变压器和管道的示例。请参阅《[用户指南》](https://scikit-learn.org/stable/modules/compose.html#combining-estimators)。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_feature_union_thumb.png) <br/> [连结多个特征提取方法](https://scikit-learn.org/stable/auto_examples/compose/plot_feature_union.html#sphx-glr-auto-examples-compose-plot-feature-union-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id214) | ![](img/sphx_glr_plot_digits_pipe_thumb.png) <br/> [流水线：链接PCA和逻辑回归](https://scikit-learn.org/stable/auto_examples/compose/plot_digits_pipe.html#sphx-glr-auto-examples-compose-plot-digits-pipe-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id215) | ![](img/sphx_glr_plot_column_transformer_mixed_types_thumb.png) <br/> [混合类型的列转换器](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer_mixed_types.html#sphx-glr-auto-examples-compose-plot-column-transformer-mixed-types-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id216) | ![](img/sphx_glr_plot_compare_reduction_thumb.png) <br/> [使用Pipeline和GridSearchCV选择降维](https://scikit-learn.org/stable/auto_examples/compose/plot_compare_reduction.html#sphx-glr-auto-examples-compose-plot-compare-reduction-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id217) |
-| ![](img/sphx_glr_plot_column_transformer_thumb.png) <br/> [具有异构数据源的列转换器](https://scikit-learn.org/stable/auto_examples/compose/plot_column_transformer.html#sphx-glr-auto-examples-compose-plot-column-transformer-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id218) | ![](img/sphx_glr_plot_transformed_target_thumb.png) <br/> [在回归模型中转换目标的效果](https://scikit-learn.org/stable/auto_examples/compose/plot_transformed_target.html#sphx-glr-auto-examples-compose-plot-transformed-target-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id219) |
-
-
-## 预处理
-
-有关[`sklearn.preprocessing`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.preprocessing "sklearn。预处理")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_function_transformer_thumb.png) <br/> [使用FunctionTransformer选择列](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_function_transformer.html#sphx-glr-auto-examples-preprocessing-plot-function-transformer-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id220) | ![](img/sphx_glr_plot_discretization_thumb.png) <br/> [使用KBinsDiscretizer离散化连续特征](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization.html#sphx-glr-auto-examples-preprocessing-plot-discretization-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id221) | ![](img/sphx_glr_plot_discretization_strategies_thumb.png) <br/> [演示KBinsDiscretizer的不同策略](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_strategies.html#sphx-glr-auto-examples-preprocessing-plot-discretization-strategies-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id222) | ![](img/sphx_glr_plot_scaling_importance_thumb.png) <br/> [特征缩放的重要性](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_scaling_importance.html#sphx-glr-auto-examples-preprocessing-plot-scaling-importance-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id223) |
-| ![](img/sphx_glr_plot_map_data_to_normal_thumb.png) <br/> [地图数据正态分布](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_map_data_to_normal.html#sphx-glr-auto-examples-preprocessing-plot-map-data-to-normal-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id224) | ![](img/sphx_glr_plot_discretization_classification_thumb.png) <br/> [功能离散](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_discretization_classification.html#sphx-glr-auto-examples-preprocessing-plot-discretization-classification-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id225) | ![](img/sphx_glr_plot_all_scaling_thumb.png) <br/> [比较不同缩放器对数据与异常值的影响](https://scikit-learn.org/stable/auto_examples/preprocessing/plot_all_scaling.html#sphx-glr-auto-examples-preprocessing-plot-all-scaling-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id226) |
-
-
-## 发布要点
-
-这些示例说明了scikit-learn发行版的主要功能。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_release_highlights_0_22_0_thumb.png) <br/> [scikit-learn 0.22的发行要点](https://scikit-learn.org/stable/auto_examples/release_highlights/plot_release_highlights_0_22_0.html#sphx-glr-auto-examples-release-highlights-plot-release-highlights-0-22-0-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id227) |
-
-
-## 半监督分类
-
-有关[`sklearn.semi_supervised`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.semi_supervised "sklearn.semi_supervised")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_label_propagation_versus_svm_iris_thumb.png) <br/> [Iris数据集上标签传播与SVM的决策边界](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_versus_svm_iris.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-versus-svm-iris-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id228) | ![](img/sphx_glr_plot_label_propagation_structure_thumb.png) <br/> [标签传播学习复杂的结构](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_structure.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-structure-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id229) | ![](img/sphx_glr_plot_label_propagation_digits_thumb.png) <br/> [标签传播数字：演示性能](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id230) | ![](img/sphx_glr_plot_label_propagation_digits_active_learning_thumb.png) <br/> [标签传播数字主动学习](https://scikit-learn.org/stable/auto_examples/semi_supervised/plot_label_propagation_digits_active_learning.html#sphx-glr-auto-examples-semi-supervised-plot-label-propagation-digits-active-learning-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id231) |
-
-
-## 支持向量机
-
-有关[`sklearn.svm`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.svm "sklearn.svm")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_svm_nonlinear_thumb.png) <br/> [非线性](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_nonlinear.html#sphx-glr-auto-examples-svm-plot-svm-nonlinear-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id232) | ![](img/sphx_glr_plot_separating_hyperplane_thumb.png) <br/> [SVM：最大余量分隔超平面](https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id233) | ![](img/sphx_glr_plot_custom_kernel_thumb.png) <br/> [具有自定义内核的](https://scikit-learn.org/stable/auto_examples/svm/plot_custom_kernel.html#sphx-glr-auto-examples-svm-plot-custom-kernel-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id234) | ![](img/sphx_glr_plot_linearsvc_support_vectors_thumb.png) <br/> [在LinearSVC中绘制支持向量](https://scikit-learn.org/stable/auto_examples/svm/plot_linearsvc_support_vectors.html#sphx-glr-auto-examples-svm-plot-linearsvc-support-vectors-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id235) |
-| ![](img/sphx_glr_plot_svm_tie_breaking_thumb.png) <br/> [SVM中断示例](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_tie_breaking.html#sphx-glr-auto-examples-svm-plot-svm-tie-breaking-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id236) | ![](img/sphx_glr_plot_weighted_samples_thumb.png) <br/> [SVM：加权样本](https://scikit-learn.org/stable/auto_examples/svm/plot_weighted_samples.html#sphx-glr-auto-examples-svm-plot-weighted-samples-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id237) | ![](img/sphx_glr_plot_separating_hyperplane_unbalanced_thumb.png) <br/> [SVM：为不平衡的类分离超平面](https://scikit-learn.org/stable/auto_examples/svm/plot_separating_hyperplane_unbalanced.html#sphx-glr-auto-examples-svm-plot-separating-hyperplane-unbalanced-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id238) | ![](img/sphx_glr_plot_svm_kernels_thumb.png) <br/> [SVM内核](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_kernels.html#sphx-glr-auto-examples-svm-plot-svm-kernels-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id239) |
-| ![](img/sphx_glr_plot_svm_anova_thumb.png) <br/> [SVM-Anova：具有单变量特征选择的](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_anova.html#sphx-glr-auto-examples-svm-plot-svm-anova-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id240) | ![](img/sphx_glr_plot_svm_regression_thumb.png) <br/> [使用线性和非线性内核支持向量回归（SVR）](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_regression.html#sphx-glr-auto-examples-svm-plot-svm-regression-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id241) | ![](img/sphx_glr_plot_svm_margin_thumb.png) <br/> [SVM保证金示例](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_margin.html#sphx-glr-auto-examples-svm-plot-svm-margin-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id242) | ![](img/sphx_glr_plot_oneclass_thumb.png) <br/> [具有非线性内核（RBF）的一类](https://scikit-learn.org/stable/auto_examples/svm/plot_oneclass.html#sphx-glr-auto-examples-svm-plot-oneclass-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id243) |
-| ![](img/sphx_glr_plot_iris_svc_thumb.png) <br/> [在虹膜数据集中绘制不同的SVM分类器](https://scikit-learn.org/stable/auto_examples/svm/plot_iris_svc.html#sphx-glr-auto-examples-svm-plot-iris-svc-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id244) | ![](img/sphx_glr_plot_svm_scale_c_thumb.png) <br/> [扩展SVC的正则化参数](https://scikit-learn.org/stable/auto_examples/svm/plot_svm_scale_c.html#sphx-glr-auto-examples-svm-plot-svm-scale-c-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id245) | ![](img/sphx_glr_plot_rbf_parameters_thumb.png) <br/> [RBF SVM参数](https://scikit-learn.org/stable/auto_examples/svm/plot_rbf_parameters.html#sphx-glr-auto-examples-svm-plot-rbf-parameters-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id246) |
-
-
-## 教程练习
-
-教程练习。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_digits_classification_exercise_thumb.png) <br/> [数字分类练习](https://scikit-learn.org/stable/auto_examples/exercises/plot_digits_classification_exercise.html#sphx-glr-auto-examples-exercises-plot-digits-classification-exercise-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id247) | ![](img/sphx_glr_plot_cv_digits_thumb.png) <br/> [交叉验证数字数据集练习](https://scikit-learn.org/stable/auto_examples/exercises/plot_cv_digits.html#sphx-glr-auto-examples-exercises-plot-cv-digits-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id248) | ![](img/sphx_glr_plot_iris_exercise_thumb.png) <br/> [SVM练习](https://scikit-learn.org/stable/auto_examples/exercises/plot_iris_exercise.html#sphx-glr-auto-examples-exercises-plot-iris-exercise-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id249) | ![](img/sphx_glr_plot_cv_diabetes_thumb.png) <br/> [糖尿病运动数据集交叉验证](https://scikit-learn.org/stable/auto_examples/exercises/plot_cv_diabetes.html#sphx-glr-auto-examples-exercises-plot-cv-diabetes-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id250) |
-
-
-## 文本文档工作
-
-有关[`sklearn.feature_extraction.text`](https://scikit-learn.org/stable/modules/classes.html#module-sklearn.feature_extraction.text "sklearn.feature_extraction.text")模块的示例。
-
-| | | | |
-| -- | -- | -- | -- |
-| ![](img/sphx_glr_plot_hashing_vs_dict_vectorizer_thumb.png) <br/> [FeatureHasher和DictVectorizer比较](https://scikit-learn.org/stable/auto_examples/text/plot_hashing_vs_dict_vectorizer.html#sphx-glr-auto-examples-text-plot-hashing-vs-dict-vectorizer-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id251) | ![](img/sphx_glr_plot_document_clustering_thumb.png) <br/> [使用k-means聚类文本文档](https://scikit-learn.org/stable/auto_examples/text/plot_document_clustering.html#sphx-glr-auto-examples-text-plot-document-clustering-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id252) | ![](img/sphx_glr_plot_document_classification_20newsgroups_thumb.png) <br/> [使用稀疏特征对文本文档进行分类](https://scikit-learn.org/stable/auto_examples/text/plot_document_classification_20newsgroups.html#sphx-glr-auto-examples-text-plot-document-classification-20newsgroups-py)[](https://scikit-learn.org/stable/auto_examples/index.html#id253)

Python/sklearn/sklearn-doc-zh/examples/SUMMARY.md

@@ -1,41 +0,0 @@
-* 其他示例
-* 双聚类
-    * [频谱共聚算法演示](Biclustering/a_demo_of_the_spectral_co-clustering_algorithm.md)
-    * [频谱双聚类算法演示](Biclustering/a_demo_of_the_spectral_clustering_algorithm.md)
-    * [使用频谱共聚算法对文档进行聚合](Biclustering/biclustering_documents_with_the_spectral_co-clustering_algorithm.md)
-* 校准
-* 分类
-* 多聚类
-* 协方差估计
-* 交叉分解
-* 数据集示例
-* 决策树
-* 分解
-* 集成方法
-* 基于真实数据集的示例
-* 特征选择
-* 高斯混合模型
-* 高斯机器学习过程
-* 广义线性模型
-    * [Lasso 和弹性网络在稀疏信号上的表现](Generalized_Linear_Models/plot_lasso_and_elasticnet.md)
-    * [Lasso 和弹性网络](Generalized_Linear_Models/plot_lasso_coordinate_descent_path.md)
-    * [Lasso 模型选择：交叉验证 / AIC / BIC](Generalized_Linear_Models/plot_lasso_model_selection.md)
-    * [多任务 Lasso 实现联合特征选择](Generalized_Linear_Models/plot_multi_task_lasso_support.md)
-    * [线性回归示例](Generalized_Linear_Models/plot_ols.md)
-    * [岭系数对回归系数的影响](Generalized_Linear_Models/plot_ridge_path.md)
-    * [压缩感知：用L1先验概率进行断层重建](Generalized_Linear_Models/plot_tomography_l1_reconstruction.md)
-* 检查
-* 流行学习
-* 缺失值插补
-* 选型
-* 多输出方法
-* 最近邻
-* 神经网络
-* 管道和复合估计器
-* 预处理
-* 发布要点
-* 半监督分类
-* 支持向量机
-* 建成练习
-* 文本文档工作
-    * [分类特征稀疏的文本](Generalized_Linear_Models/plot_document_classification_20newsgroups.md)

Python/sklearn/sklearn-doc-zh/examples/book.json

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