<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>回归 on PlumePHP</title><link>https://plumephp.com/tags/%E5%9B%9E%E5%BD%92/</link><description>Recent content in 回归 on PlumePHP</description><generator>Hugo</generator><language>zh-CN</language><lastBuildDate>Thu, 13 Aug 2026 11:02:00 +0800</lastBuildDate><atom:link href="https://plumephp.com/tags/%E5%9B%9E%E5%BD%92/index.xml" rel="self" type="application/rss+xml"/><item><title>02. 监督学习算法详解</title><link>https://plumephp.com/ai-supervised-learning/</link><pubDate>Thu, 13 Aug 2026 11:02:00 +0800</pubDate><guid>https://plumephp.com/ai-supervised-learning/</guid><description>&lt;p&gt;监督学习是机器学习最广泛应用的范式。本文深入讲解从经典线性模型到现代梯度提升树的核心算法，包括数学原理、实现细节与调优策略。&lt;/p&gt;
&lt;h2 id="1-线性回归-linear-regression"&gt;1. 线性回归 (Linear Regression)&lt;/h2&gt;
&lt;h3 id="11-最小二乘法"&gt;1.1 最小二乘法&lt;/h3&gt;
&lt;p&gt;假设目标 $y$ 与特征 $X$ 呈线性关系：$y = Xw + b$&lt;/p&gt;</description></item></channel></rss>