{"id":18009275,"url":"https://github.com/deftruth/statistic-learning-r-note","last_synced_at":"2025-03-18T07:09:22.016Z","repository":{"id":39342375,"uuid":"168307269","full_name":"DefTruth/statistic-learning-R-note","owner":"DefTruth","description":"📒《统计学习方法-李航: 笔记-从原理到实现，基于R语言》200页PDF，各种手推公式细节讲解，R语言实现. 🎉🎉","archived":false,"fork":false,"pushed_at":"2025-02-07T08:48:11.000Z","size":24393,"stargazers_count":439,"open_issues_count":2,"forks_count":55,"subscribers_count":3,"default_branch":"master","last_synced_at":"2025-03-11T06:15:45.454Z","etag":null,"topics":["lihang","ml","r","statistic-notes","statistics","statistics-learning"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/DefTruth.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2019-01-30T08:31:46.000Z","updated_at":"2025-03-09T09:38:37.000Z","dependencies_parsed_at":"2024-10-30T02:29:34.040Z","dependency_job_id":null,"html_url":"https://github.com/DefTruth/statistic-learning-R-note","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DefTruth%2Fstatistic-learning-R-note","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DefTruth%2Fstatistic-learning-R-note/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DefTruth%2Fstatistic-learning-R-note/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/DefTruth%2Fstatistic-learning-R-note/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/DefTruth","download_url":"https://codeload.github.com/DefTruth/statistic-learning-R-note/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244173549,"owners_count":20410300,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["lihang","ml","r","statistic-notes","statistics","statistics-learning"],"created_at":"2024-10-30T02:08:58.849Z","updated_at":"2025-03-18T07:09:21.985Z","avatar_url":"https://github.com/DefTruth.png","language":null,"readme":"\u003c!--\n![image](https://github.com/DefTruth/statistic-learning-R-note/assets/31974251/07297b6a-d94c-4db0-aaef-8132071c94cb)\n--\u003e\n\n![image](https://github.com/user-attachments/assets/c7ff6abe-8ae1-449e-9b07-7d1b49b9cf48)\n\n\u003cdiv align='center'\u003e\n  \u003cimg src=https://img.shields.io/github/downloads/DefTruth/statistic-learning-R-note/total?color=ccf\u0026label=downloads\u0026logo=github\u0026logoColor=lightgrey \u003e\n  \u003cimg src=https://img.shields.io/github/forks/DefTruth/statistic-learning-R-note.svg?style=social \u003e\n  \u003cimg src=https://img.shields.io/github/stars/DefTruth/statistic-learning-R-note.svg?style=social \u003e\n  \u003cimg src=https://img.shields.io/badge/PDF-avaliable-brightgreen.svg \u003e\n  \u003cimg src=https://img.shields.io/badge/Release-v1.0-brightgreen.svg \u003e\n  \u003cimg src=https://img.shields.io/badge/License-GPLv3.0-turquoise.svg \u003e\n \u003c/div\u003e   \n\n## News 👇👇\nMost of my time now is focused on **LLM/VLM** Inference. Please check 📖[Awesome-LLM-Inference](https://github.com/DefTruth/Awesome-LLM-Inference)  ![](https://img.shields.io/github/stars/DefTruth/Awesome-LLM-Inference.svg?style=social), 📖[Awesome-SD-Inference](https://github.com/DefTruth/Awesome-SD-Inference)  ![](https://img.shields.io/github/stars/DefTruth/Awesome-SD-Inference.svg?style=social) and 📖[CUDA-Learn-Notes](https://github.com/DefTruth/CUDA-Learn-Notes)  ![](https://img.shields.io/github/stars/DefTruth/CUDA-Learn-Notes.svg?style=social) for more details.\n\n## 📒Introduction\n 《统计学习方法-李航: 笔记-从原理到实现》 这是一份非常详细的学习笔记，200页，各种手推公式细节讲解，整理成PDF，有详细的目录，可结合《统计学习方法》提高学习效率。如果觉得有用，不妨给个🌟Star支持一下吧~\n\n## ©️Citations \n\n```BibTeX\n@misc{statistic-learning-R-note@2019,\n  title={statistic-learning-R-note: A detail note book of statistic-learning with R codes},\n  url={https://github.com/DefTruth/statistic-learning-R-note},\n  note={Open-source software available at https://github.com/DefTruth/statistic-learning-R-note},\n  author={DefTruth},\n  year={2019}\n}\n```\n\n## 🎉Download PDFs \n- [李航《统计学习方法》笔记--从原理到实现：基于R.pdf 👆🏻\u003c点击下载!\u003e](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n```shell\nwget https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf\n```\n\n## 📖Contents  \n\n![image](https://github.com/DefTruth/statistic-learning-R-note/assets/31974251/561384a1-fbc3-40ed-af62-98268904f387)\n\n- 第一章 统计学习方法概述\n  - [1.6.2 泛化误差上界（P16-P17）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n  - [1.4.2 过拟合与模型选择（P11）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n- 第二章 感知机  \n  - [2.3.1 感知机算法的原始形式](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n  - [2.3.2 算法的收敛性（Novikoff 定理）（P31-P33）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n  - [2.3.3 感知机学习算法的对偶形式（P33-P34）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n  - [2.3.1 感知机算法的原始形式（P28-P29）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [2.3.3 感知机学习算法的对偶形式（P33-P34）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- 第三章 K 近邻法\n  - [3.2.2 距离度量（P39）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)   \n  - [3.3.1 构造kd 树（P41-P42）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)   \n- 第四章 朴素贝叶斯算法   \n  - [4.1.1 基本方法（P47-P48）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n  - [4.1.2 后验概率最大化的含义（P48-49）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [4.2.1 极大似然估计（P49）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n  - [4.2.2 学习与分类算法（P50-51）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- 第五章 决策树   \n  - [5.2.2 信息增益（P60-P61）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)   \n  - [5.2.3 信息增益比（P63）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [5.3.1 ID3 算法/C4.5 算法（P63-P65）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [5.4 决策树的剪枝（P65-P67）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [5.5.1 CART 生成（P68-P71）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [5.5.2 CART 剪枝（P72-P73）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n- 第六章 逻辑斯蒂回归与最大熵模型   \n  - [6.1.3 逻辑斯蒂回归模型的参数估计（P79）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [6.2.3 最大熵模型的学习（P83-P85）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [6.2.4 极大似然估计（P87）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [6.3.1 改进的迭代尺度算法（P89-P91）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n- 第七章 支持向量机  \n  - [7.1.3 间隔最大化（P101）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [7.1.4 学习的对偶算法（P104）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [7.2.3 支持向量（P113）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [7.4 序列最小最优化算法（P126）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n- 第八章 提升方法  \n  - [8.1.2 Adaboost 算法（P139）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [8.2 AdaBoost 算法的训练误差分析（P142-P145）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [8.3.2 前向分步算法与 AdaBoost（P145-P146）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [8.4.3 梯度提升（P151）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [8.1.3 AdaBoost 的例子（P140）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n- 第九章 EM 算法及其推广  \n  - [9.2 EM 算法的收敛性（P161）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [9.3.1 高斯混合模型（P163）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [9.4 EM 算法的推广（P167）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [9.3.1 高斯混合模型的 EM 算法（165）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n- 第十章 隐马尔可夫模型 \n  - [10.2.2 前向算法（P175-P176）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.2.3 后向算法（P178-P179）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.4.2 维特比算法（P185）]() \n  - [10.2.4 一些概率与期望值的计算（P179-P180）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.2.2 前向算法（P175-P177）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.2.3 后向算法（P178）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.2.4 一些概率与期望值的计算（P179-P178）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.3.1 监督学习方法（P180）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.3.2 Baum-Welch 算法（P181-P184）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.4.1 近似算法（P184）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf) \n  - [10.4.2 维特比算法（P185-P186）](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n- 十一章 条件随机场  \n- 参考文献\n- [附录 1 例 1.1 的 R 实现/训练误差与预测误差的对比](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n- [附录 2 线性可分/不可分感知机的 R 实现 ](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 3 离散特征的 2 维平衡 kd 树 R 代码 ](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 4 离散特征的朴素贝叶斯法 R 代码](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 5 决策树的实现的 R 代码](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 6 逻辑斯蒂回归及最大熵模型的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 7 基于 SMO 算法的支持向量机的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 8 提升算法的 R 代码](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 9 EM 算法的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)  \n- [附录 10 HMM 模型的 R 实现](https://github.com/DefTruth/statistic-learning-R-note/releases/download/v0.1.0/statistic.learning.R.Note.v0.1.0.pdf)\n\n## ©️License  \n\nGNU General Public License v3.0  \n\n## 🎉Contribute  \n\n🌟如果觉得有用，不妨给个🌟👆🏻Star支持一下吧~\n\n\u003cdiv align='center'\u003e\n  \u003cimg width=\"400\" height=\"250\" alt=\"v02\" src=\"https://github.com/DefTruth/statistic-learning-R-note/assets/31974251/561384a1-fbc3-40ed-af62-98268904f387\"\u003e  \n\u003ca href=\"https://star-history.com/#DefTruth/statistic-learning-R-note\u0026Date\"\u003e\n  \u003cpicture align='center'\u003e\n    \u003csource media=\"(prefers-color-scheme: dark)\" srcset=\"https://api.star-history.com/svg?repos=DefTruth/statistic-learning-R-note\u0026type=Date\u0026theme=dark\" /\u003e\n    \u003csource media=\"(prefers-color-scheme: light)\" srcset=\"https://api.star-history.com/svg?repos=DefTruth/statistic-learning-R-note\u0026type=Date\" /\u003e\n    \u003cimg width=\"400\" height=\"250\" alt=\"Star History Chart\" src=\"https://api.star-history.com/svg?repos=DefTruth/statistic-learning-R-note\u0026type=Date\" /\u003e\n  \u003c/picture\u003e\n\u003c/a\u003e\n\u003c/div\u003e\n\n\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeftruth%2Fstatistic-learning-r-note","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fdeftruth%2Fstatistic-learning-r-note","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fdeftruth%2Fstatistic-learning-r-note/lists"}