<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>线性回归 on QiYue's Blog</title><link>https://hello-github-ui-github-io.onrender.com/tags/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/</link><description>Recent content in 线性回归 on QiYue's Blog</description><generator>Hugo -- gohugo.io</generator><language>zh-cn</language><copyright>&amp;copy; 2026 QiYue's Blog</copyright><lastBuildDate>Tue, 20 Oct 2026 13:11:00 +0800</lastBuildDate><atom:link href="https://hello-github-ui-github-io.onrender.com/tags/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92/index.xml" rel="self" type="application/rss+xml"/><item><title>线性回归-多因子房价预测</title><link>https://hello-github-ui-github-io.onrender.com/posts/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92-%E5%A4%9A%E5%9B%A0%E5%AD%90%E6%88%BF%E4%BB%B7%E9%A2%84%E6%B5%8B/</link><pubDate>Tue, 20 Oct 2026 13:11:00 +0800</pubDate><guid>https://hello-github-ui-github-io.onrender.com/posts/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92-%E5%A4%9A%E5%9B%A0%E5%AD%90%E6%88%BF%E4%BB%B7%E9%A2%84%E6%B5%8B/</guid><description/></item><item><title>线性回归-单因子实战</title><link>https://hello-github-ui-github-io.onrender.com/posts/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92-%E5%8D%95%E5%9B%A0%E5%AD%90%E5%AE%9E%E6%88%98/</link><pubDate>Tue, 20 Oct 2026 08:09:11 +0800</pubDate><guid>https://hello-github-ui-github-io.onrender.com/posts/%E7%BA%BF%E6%80%A7%E5%9B%9E%E5%BD%92-%E5%8D%95%E5%9B%A0%E5%AD%90%E5%AE%9E%E6%88%98/</guid><description>&lt;h1 class="relative group"&gt;1、目标&#10; &lt;div id="1目标" class="anchor"&gt;&lt;/div&gt;&#10; &#10; &lt;span&#10; class="absolute top-0 w-6 transition-opacity opacity-0 -start-6 not-prose group-hover:opacity-100 select-none"&gt;&#10; &lt;a class="text-primary-300 dark:text-neutral-700 !no-underline" href="#1%e7%9b%ae%e6%a0%87" aria-label="锚点"&gt;#&lt;/a&gt;&#10; &lt;/span&gt;&#10; &#10;&lt;/h1&gt;&#10;&lt;blockquote&gt;&lt;p&gt;&lt;strong&gt;==LR 实现线性预测==&lt;/strong&gt;&#10;&lt;strong&gt;任务&lt;/strong&gt;：&#10;基于 &lt;code&gt;generated_data.csv&lt;/code&gt; 数据，建立线性回归模型，预测 &lt;code&gt;x=3.5&lt;/code&gt; 对应的 &lt;code&gt;y&lt;/code&gt; 的值，评估模型表现。&lt;/p&gt;</description></item></channel></rss>