{"action":"opened","issue":{"url":"https://api.github.com/repos/Hermioneyuzijin/hello-world/issues/6","repository_url":"https://api.github.com/repos/Hermioneyuzijin/hello-world","labels_url":"https://api.github.com/repos/Hermioneyuzijin/hello-world/issues/6/labels{/name}","comments_url":"https://api.github.com/repos/Hermioneyuzijin/hello-world/issues/6/comments","events_url":"https://api.github.com/repos/Hermioneyuzijin/hello-world/issues/6/events","html_url":"https://github.com/Hermioneyuzijin/hello-world/issues/6","id":281605802,"number":6,"title":"机器学习之线性回归1","user":{"login":"Hermioneyuzijin","id":10783685,"avatar_url":"https://avatars2.githubusercontent.com/u/10783685?v=4","gravatar_id":"","url":"https://api.github.com/users/Hermioneyuzijin","html_url":"https://github.com/Hermioneyuzijin","followers_url":"https://api.github.com/users/Hermioneyuzijin/followers","following_url":"https://api.github.com/users/Hermioneyuzijin/following{/other_user}","gists_url":"https://api.github.com/users/Hermioneyuzijin/gists{/gist_id}","starred_url":"https://api.github.com/users/Hermioneyuzijin/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/Hermioneyuzijin/subscriptions","organizations_url":"https://api.github.com/users/Hermioneyuzijin/orgs","repos_url":"https://api.github.com/users/Hermioneyuzijin/repos","events_url":"https://api.github.com/users/Hermioneyuzijin/events{/privacy}","received_events_url":"https://api.github.com/users/Hermioneyuzijin/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":0,"created_at":"2017-12-13T02:37:10Z","updated_at":"2017-12-13T02:37:10Z","closed_at":null,"author_association":"OWNER","body":"# Linear Regression\r\n## 一、回归定义\r\n给出一个点集D，用一个函数去拟合这个点集，并且使得点集与拟合函数间的误差最小，如果这个函数曲线是一条直线，就是线性回归。\r\n## 二、回归分类\r\n### 按照数据类型\r\n1. 离散型：决策树模型 \r\n2. 连续型\r\n### 按照回归变量\r\n1. 一元线性回归\r\n2. 多元线性回归：![多元线性回归](http://upload-images.jianshu.io/upload_images/1111419-13d9b52e2d7263f8)\r\n## 三、求解\r\n1.最小二乘法\r\n## 四、python实现\r\n## 五、预测未来"}}
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