<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>ML Technique on 中文AI术语词典</title><link>https://terms-en.ai-term-hub.com/zh/tags/ml-technique/</link><description>Recent content in ML Technique 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/ml-technique/index.xml" rel="self" type="application/rss+xml"/><item><title>代理模型</title><link>https://terms-en.ai-term-hub.com/zh/terms/surrogate_model/</link><pubDate>Sat, 18 Jul 2026 11:35:31 +0000</pubDate><guid>https://terms-en.ai-term-hub.com/zh/terms/surrogate_model/</guid><description>&lt;h2 id="definition">Definition&lt;/h2>
&lt;p>在机器学习和优化中，代理模型作为难以直接评估的目标函数的代理。它通过原始模型的输入-输出对进行训练，以预测目标函数的行为。&lt;/p>
&lt;h3 id="summary">Summary&lt;/h3>
&lt;p>一种简化的数学模型，用于近似更复杂、计算成本高或不可访问的黑盒模型的行为。&lt;/p>
&lt;h2 id="key-concepts">Key Concepts&lt;/h2>
&lt;ul>
&lt;li>模型近似&lt;/li>
&lt;li>黑盒优化&lt;/li>
&lt;li>计算效率&lt;/li>
&lt;li>代理模型&lt;/li>
&lt;/ul>
&lt;h2 id="use-cases">Use Cases&lt;/h2>
&lt;ul>
&lt;li>超参数优化&lt;/li>
&lt;li>工程设计模拟加速&lt;/li>
&lt;li>复杂系统的敏感性分析&lt;/li>
&lt;/ul>
&lt;h2 id="code-example">Code Example&lt;/h2>
&lt;div class="highlight">&lt;pre tabindex="0" style="color:#f8f8f2;background-color:#272822;-moz-tab-size:4;-o-tab-size:4;tab-size:4;">&lt;code class="language-python" data-lang="python">&lt;span style="display:flex;">&lt;span>&lt;span style="color:#f92672">from&lt;/span> sklearn.gaussian_process &lt;span style="color:#f92672">import&lt;/span> GaussianProcessRegressor
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>&lt;span style="color:#f92672">import&lt;/span> numpy &lt;span style="color:#66d9ef">as&lt;/span> np
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>&lt;span style="color:#75715e"># Simple surrogate for a noisy function&lt;/span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>X &lt;span style="color:#f92672">=&lt;/span> np&lt;span style="color:#f92672">.&lt;/span>array([[&lt;span style="color:#ae81ff">1&lt;/span>], [&lt;span style="color:#ae81ff">2&lt;/span>], [&lt;span style="color:#ae81ff">3&lt;/span>], [&lt;span style="color:#ae81ff">4&lt;/span>]])
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>y &lt;span style="color:#f92672">=&lt;/span> np&lt;span style="color:#f92672">.&lt;/span>array([&lt;span style="color:#ae81ff">2.1&lt;/span>, &lt;span style="color:#ae81ff">3.9&lt;/span>, &lt;span style="color:#ae81ff">6.2&lt;/span>, &lt;span style="color:#ae81ff">7.8&lt;/span>])
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>surrogate &lt;span style="color:#f92672">=&lt;/span> GaussianProcessRegressor()
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>surrogate&lt;span style="color:#f92672">.&lt;/span>fit(X, y)
&lt;/span>&lt;/span>&lt;span style="display:flex;">&lt;span>prediction &lt;span style="color:#f92672">=&lt;/span> surrogate&lt;span style="color:#f92672">.&lt;/span>predict(np&lt;span style="color:#f92672">.&lt;/span>array([[&lt;span style="color:#ae81ff">2.5&lt;/span>]]))
&lt;/span>&lt;/span>&lt;/code>&lt;/pre>&lt;/div>&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/bayesian-optimization-%E8%B4%9D%E5%8F%B6%E6%96%AF%E4%BC%98%E5%8C%96/">Bayesian Optimization (贝叶斯优化)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/gaussian-process-%E9%AB%98%E6%96%AF%E8%BF%87%E7%A8%8B/">Gaussian Process (高斯过程)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/black-box-function-%E9%BB%91%E7%9B%92%E5%87%BD%E6%95%B0/">Black-Box Function (黑盒函数)&lt;/a>&lt;/li>
&lt;li>&lt;a href="https://terms-en.ai-term-hub.com/en/terms/emulator-%E6%A8%A1%E6%8B%9F%E5%99%A8/">Emulator (模拟器)&lt;/a>&lt;/li>
&lt;/ul></description></item></channel></rss>