<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Trees on 中文AI术语词典</title><link>https://terms-en.ai-term-hub.com/zh/tags/trees/</link><description>Recent content in Trees on 中文AI术语词典</description><generator>Hugo</generator><language>zh-cn</language><lastBuildDate>Sat, 18 Jul 2026 11:44:45 +0000</lastBuildDate><atom:link href="https://terms-en.ai-term-hub.com/zh/tags/trees/index.xml" rel="self" type="application/rss+xml"/><item><title>决策树剪枝</title><link>https://terms-en.ai-term-hub.com/zh/terms/decision_tree_pruning/</link><pubDate>Sat, 18 Jul 2026 11:13:51 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/zh/terms/decision_tree_pruning/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>剪枝是一种用于防止决策树模型过拟合的方法，通过移除具有弱预测能力的分支来实现。它可以以预剪枝（提前停止树的生长）或后剪枝的方式执行&amp;hellip;&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>一种通过移除对分类实例贡献较小的部分来减小决策树规模的技术。&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>防止过拟合&lt;/li>
&lt;li>预剪枝&lt;/li>
&lt;li>后剪枝&lt;/li>
&lt;li>模型复杂度&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>提高模型泛化能力&lt;/li>
&lt;li>降低推理延迟&lt;/li>
&lt;li>简化规则提取&lt;/li>
&lt;/ul>
&lt;h2 id="related-terms">Related Terms&lt;/h2>
&lt;ul>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/%E6%AD%A3%E5%88%99%E5%8C%96-regularization/">正则化 (Regularization)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81-cross-validation/">交叉验证 (Cross-validation)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/%E7%86%B5-entropy/">熵 (Entropy)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/%E4%BF%A1%E6%81%AF%E5%A2%9E%E7%9B%8A-information-gain/">信息增益 (Information gain)&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>