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<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
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<div class="flink-item-name">Twitter</div>
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<div class="flink-item-desc" title="社交分享平台">社交分享平台</div>
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||||
</a>
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<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
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</ul></div><div class="card-widget card-tags"><div class="item-headline"><i class="fas fa-tags"></i><span>标签</span></div><div class="card-tag-cloud"><a href="/tags/C-C/" style="font-size: 1.1em; color: #999">C C++</a> <a href="/tags/decisiontree/" style="font-size: 1.1em; color: #999">decisiontree</a> <a href="/tags/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/" style="font-size: 1.1em; color: #999">古文观止</a> <a href="/tags/KNN/" style="font-size: 1.1em; color: #999">KNN</a> <a href="/tags/uniapp/" style="font-size: 1.5em; color: #99a9bf">uniapp</a> <a href="/tags/logistic-regression/" style="font-size: 1.1em; color: #999">logistic-regression</a> <a href="/tags/%E7%BD%91%E7%BB%9C%E4%BB%A3%E7%90%86/" style="font-size: 1.1em; color: #999">网络代理</a> <a href="/tags/ensemble-learning/" style="font-size: 1.1em; color: #999">ensemble-learning</a> <a href="/tags/linear-regression/" style="font-size: 1.1em; color: #999">linear-regression</a></div></div><div class="card-widget card-archives"><div class="item-headline"><i class="fas fa-archive"></i><span>归档</span></div><ul class="card-archive-list"><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2025/01/"><span class="card-archive-list-date">一月 2025</span><span class="card-archive-list-count">6</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/09/"><span class="card-archive-list-date">九月 2024</span><span class="card-archive-list-count">4</span></a></li><li class="card-archive-list-item"><a class="card-archive-list-link" href="/archives/2024/08/"><span class="card-archive-list-date">八月 2024</span><span class="card-archive-list-count">8</span></a></li></ul></div><div class="card-widget card-webinfo"><div class="item-headline"><i class="fas fa-chart-line"></i><span>网站资讯</span></div><div class="webinfo"><div class="webinfo-item"><div class="item-name">文章数目 :</div><div class="item-count">18</div></div><div class="webinfo-item"><div class="item-name">已运行时间 :</div><div class="item-count" id="runtimeshow" data-publishDate="2024-07-30T16:00:00.000Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站访客数 :</div><div class="item-count" id="busuanzi_value_site_uv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">本站总访问量 :</div><div class="item-count" id="busuanzi_value_site_pv"><i class="fa-solid fa-spinner fa-spin"></i></div></div><div class="webinfo-item"><div class="item-name">最后更新时间 :</div><div class="item-count" id="last-push-date" data-lastPushDate="2025-02-08T09:43:31.930Z"><i class="fa-solid fa-spinner fa-spin"></i></div></div></div></div></div></div></main><footer id="footer" style="background: transparent"><div id="footer-wrap"><div class="copyright">©2024 - 2025 By shenjianZ</div><div class="framework-info"><span>框架 </span><a target="_blank" rel="noopener" href="https://hexo.io">Hexo</a><span class="footer-separator">|</span><span>主题 </span><a target="_blank" rel="noopener" href="https://github.com/jerryc127/hexo-theme-butterfly">Butterfly</a></div><div class="footer_custom_text"><a target="_blank" rel="noopener" href="https://beian.miit.gov.cn/#/Integrated/recordQuery"><img class="icp-icon" src="https://beian.mps.gov.cn/img/logo01.dd7ff50e.png"><span>备案号：豫ICP备2023019300号</span></a></div></div></footer></div><div id="rightside"><div id="rightside-config-hide"><button id="darkmode" type="button" title="浅色和深色模式转换"><i class="fas fa-adjust"></i></button><button id="hide-aside-btn" type="button" title="单栏和双栏切换"><i class="fas fa-arrows-alt-h"></i></button></div><div id="rightside-config-show"><button id="rightside-config" type="button" title="设置"><i class="fas fa-cog fa-spin"></i></button><button id="go-up" type="button" title="回到顶部"><span class="scroll-percent"></span><i class="fas fa-arrow-up"></i></button></div></div><div><script src="/js/utils.js?v=4.13.0"></script><script src="/js/main.js?v=4.13.0"></script><script src="https://cdn.jsdelivr.net/npm/@fancyapps/ui@5.0.33/dist/fancybox/fancybox.umd.min.js"></script><div class="js-pjax"></div><script src="https://cdn.jsdelivr.net/npm/butterfly-extsrc@1.1.3/dist/activate-power-mode.min.js"></script><script>POWERMODE.colorful = true;
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POWERMODE.shake = true;
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}</script><script>(win=>{
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win.saveToLocal = {
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set: (key, value, ttl) => {
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@ -212,14 +212,14 @@ Docker pull images
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|||
deploy
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<li class="card-category-list-item "><a class="card-category-list-link" href="/categories/machinelearning/"><span class="card-category-list-name">machinelearning</span><span class="card-category-list-count">5</span></a></li><li class="card-category-list-item "><a class="card-category-list-link" href="/categories/%E5%8F%A4%E6%96%87%E8%A7%82%E6%AD%A2/"><span class="card-category-list-name">古文观止</span><span class="card-category-list-count">1</span></a></li>
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isHome: false,
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postUpdate: '2025-02-08 17:43:32'
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postUpdate: '2025-02-08 17:43:32'
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postUpdate: '2025-02-22 13:16:00'
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set: (key, value, ttl) => {
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@ -4,7 +4,8 @@ tags: decisiontree
|
|||
categories: machinelearning
|
||||
abbrlink: 95
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||||
date: 2025-01-24 12:39:59
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||||
cover: https://th.bing.com/th/id/OIP.XaPUn6eccfS_z_wTLQNFzgHaEK?w=240&h=180&c=7&r=0&o=5&dpr=1.9&pid=1.7
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cover: /img/machinelearning/decision-tree.png
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---
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### C4.5
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[ViewState]
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Mode=
|
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Vid=
|
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FolderType=Generic
|
||||
|
|
@ -4,7 +4,7 @@ tags: ensemble-learning
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|||
categories: machinelearning
|
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abbrlink: 8816
|
||||
date: 2025-01-25 15:12:08
|
||||
cover: https://th.bing.com/th/id/OIP.SZA5W6cF-tYiiZ08KZ7l7wHaEm?w=250&h=180&c=7&r=0&o=5&dpr=1.3&pid=1.7
|
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cover: /img/machinelearning/ensemble-learning.png
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---
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|
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### Bagging
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|
|
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@ -17,10 +17,10 @@ $$y = w_1x_1 + w_2x_2 + \cdot\cdot\cdot+w_nx_n$$
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### 损失函数
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为了找到最佳的线性模型，我们需要通过最小化损失函数来优化模型参数。在线性回归中，常用的损失函数是 **均方误差（MSE）**：
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$$MSE = \frac{1}{m} \sum_{i=1}^{m} (y_i - \hat{y}_i)^2$$
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- m 是样本的数量。
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$$J(\theta) = \frac{1}{2N} \sum_{i=1}^{N} (y_i - f_\theta(x_i))^2$$
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- N 是样本的数量。
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- $y_i$ 是第 i 个样本的真实值。
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- $\hat{y}_i$ 是模型预测的第 i 个样本的值。
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- $f_\theta(x_i)$ 是模型预测的第 i 个样本的值。
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|
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### 线性回归优化
|
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|
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|
|
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Reference in New Issue