fix: fix pxory network article details.

public/posts/8816.html

@@ -7,7 +7,7 @@
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@@ -153,7 +153,7 @@
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+    })(window)</script><meta name="generator" content="Hexo 7.3.0"></head><body><div id="web_bg"></div><div id="sidebar"><div id="menu-mask"></div><div id="sidebar-menus"><div class="avatar-img is-center"><img src="/img/avatar.jpg" onerror="onerror=null;src='/img/friend_404.gif'" alt="avatar"/></div><div class="sidebar-site-data site-data is-center"><a href="/archives/"><div class="headline">文章</div><div class="length-num">18</div></a><a href="/tags/"><div class="headline">标签</div><div class="length-num">9</div></a><a href="/categories/"><div class="headline">分类</div><div class="length-num">2</div></a></div><hr class="custom-hr"/><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</span></a></div></div></div></div><div class="post" id="body-wrap"><header class="post-bg fixed" id="page-header" style="background-image: url('/img/site01.jpg')"><nav id="nav"><span id="blog-info"><a href="/" title="QuickReference"><span class="site-name">QuickReference</span></a></span><div id="menus"><div id="search-button"><a class="site-page social-icon search" href="javascript:void(0);"><i class="fas fa-search fa-fw"></i><span> 搜索</span></a></div><div class="menus_items"><div class="menus_item"><a class="site-page" href="/"><i class="fa-fw fas fa-home"></i><span> Home</span></a></div><div class="menus_item"><a class="site-page" href="/archives/"><i class="fa-fw fas fa-archive"></i><span> Archives</span></a></div><div class="menus_item"><a class="site-page" href="/tags/"><i class="fa-fw fas fa-tags"></i><span> Tags</span></a></div><div class="menus_item"><a class="site-page" href="/categories/"><i class="fa-fw fas fa-folder-open"></i><span> Categories</span></a></div><div class="menus_item"><a class="site-page group" href="javascript:void(0);"><i class="fa-fw fas fa-list"></i><span> List</span><i class="fas fa-chevron-down"></i></a><ul class="menus_item_child"><li><a class="site-page child" href="/music/"><i class="fa-fw fas fa-music"></i><span> Music</span></a></li><li><a class="site-page child" href="/movies/"><i class="fa-fw fas fa-video"></i><span> Movie</span></a></li></ul></div><div class="menus_item"><a class="site-page" href="/link/"><i class="fa-fw fas fa-link"></i><span> Link</span></a></div><div class="menus_item"><a class="site-page" href="/about/"><i class="fa-fw fas fa-heart"></i><span> About</span></a></div></div><div id="toggle-menu"><a class="site-page" href="javascript:void(0);"><i class="fas fa-bars fa-fw"></i></a></div></div></nav><div id="post-info"><h1 class="post-title">集成学习</h1><div id="post-meta"><div class="meta-firstline"><span class="post-meta-date"><i class="far fa-calendar-alt fa-fw post-meta-icon"></i><span class="post-meta-label">发表于</span><time class="post-meta-date-created" datetime="2025-01-25T07:12:08.000Z" title="发表于 2025-01-25 15:12:08">2025-01-25</time><span class="post-meta-separator">|</span><i class="fas fa-history fa-fw post-meta-icon"></i><span class="post-meta-label">更新于</span><time class="post-meta-date-updated" datetime="2025-02-22T05:21:46.779Z" title="更新于 2025-02-22 13:21:46">2025-02-22</time></span><span class="post-meta-categories"><span class="post-meta-separator">|</span><i class="fas fa-inbox fa-fw post-meta-icon"></i><a class="post-meta-categories" href="/categories/machinelearning/">machinelearning</a></span></div><div class="meta-secondline"><span class="post-meta-separator">|</span><span class="post-meta-pv-cv" id="" data-flag-title="集成学习"><i class="far fa-eye fa-fw post-meta-icon"></i><span class="post-meta-label">阅读量:</span><span id="busuanzi_value_page_pv"><i class="fa-solid fa-spinner fa-spin"></i></span></span></div></div></div></header><main class="layout" id="content-inner"><div id="post"><article class="post-content" id="article-container"><h3 id="Bagging"><a href="#Bagging" class="headerlinLine truncated
 <p><code>Random-Forest</code> 就是<code>Bagging + Decisiontree</code></p>
 </blockquote>
 <figure class="highlight python"><table><tr><td class="gutter"><pre><span class="line">1</span><br><span class="line">2</span><br><span class="line">3</span><br><span class="line">4</span><br><span class="line">5</span><br><span class="line">6</span><br><span class="line">7</span><br><span class="line">8</span><br><span class="line">9</span><br><span class="line">10</span><br><span class="line">11</span><br><span class="line">12</span><br><span class="line">13</span><br><span class="line">14</span><br><span class="line">15</span><br><span class="line">16</span><br><span class="line">17</span><br><span class="line">18</span><br><span class="line">19</span><br><span class="line">20</span><br><span class="line">21</span><br><span class="line">22</span><br><span class="line">23</span><br><span class="line">24</span><br><span class="line">25</span><br><span class="line">26</span><br><span class="line">27</span><br><span class="line">28</span><br><span class="line">29</span><br><span class="line">30</span><br><span class="line">31</span><br><span class="line">32</span><br><span class="line">33</span><br><span class="line">34</span><br><span class="line">35</span><br></pre></td><td class="code"><pre><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line"><span class="keyword">import</span> pandas <span class="keyword">as</span> pd</span><br><span class="line"><span class="keyword">import</span> numpy <span class="keyword">as</span> np</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split,GridSearchCV</span><br><span class="line"><span class="keyword">from</span> sklearn.feature_extraction <span class="keyword">import</span> DictVectorizer</span><br><span class="line"><span class="keyword">from</span> sklearn.ensemble <span class="keyword">import</span> RandomForestClassifier</span><br><span class="line"><span class="comment"># 1.获取数据集 - 加载 Titanic 数据集</span></span><br><span class="line">titanic = sns.load_dataset(<span class="string">&#x27;titanic&#x27;</span>)</span><br><span class="line">missing_age_count = titanic[<span class="string">&#x27;age&#x27;</span>].isna().<span class="built_in">sum</span>()</span><br><span class="line"><span class="comment"># print(f&quot;缺失的 age 数量: &#123;missing_age_count&#125;&quot;)</span></span><br><span class="line"><span class="comment"># 2. 数据基本处理</span></span><br><span class="line"><span class="comment"># 2.1 确认特征值、目标值</span></span><br><span class="line">X = titanic[[<span class="string">&#x27;pclass&#x27;</span>,<span class="string">&#x27;age&#x27;</span>,<span class="string">&#x27;sex&#x27;</span>]]</span><br><span class="line">y = titanic[<span class="string">&#x27;survived&#x27;</span>]</span><br><span class="line"><span class="comment"># 2.2 缺失值处理</span></span><br><span class="line">X.loc[:, <span class="string">&#x27;age&#x27;</span>] = X[<span class="string">&#x27;age&#x27;</span>].fillna(value=X[<span class="string">&#x27;age&#x27;</span>].mean())  <span class="comment"># 使用 .loc 进行修改</span></span><br><span class="line"><span class="comment"># 2.3 划分数据集</span></span><br><span class="line">X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=<span class="number">22</span>)</span><br><span class="line"><span class="comment"># 3. 特征工程（字典特征提取）</span></span><br><span class="line">X_train = X_train.to_dict(orient=<span class="string">&quot;records&quot;</span>)</span><br><span class="line">X_test= X_test.to_dict(orient=<span class="string">&quot;records&quot;</span>)</span><br><span class="line">transfer = DictVectorizer()</span><br><span class="line">X_train = transfer.fit_transform(X_train)</span><br><span class="line">X_test = transfer.transform(X_test)</span><br><span class="line"><span class="comment"># 4. 机器学习 随机森林</span></span><br><span class="line">rf = RandomForestClassifier()</span><br><span class="line">gc = GridSearchCV(estimator=rf ,param_grid=&#123;<span class="string">&quot;n_estimators&quot;</span>:[<span class="number">100</span>,<span class="number">120</span>,<span class="number">300</span>],<span class="string">&quot;max_depth&quot;</span>:[<span class="number">3</span>,<span class="number">7</span>,<span class="number">11</span>]&#125;,cv=<span class="number">3</span>)</span><br><span class="line">gc.fit(X_train,y_train)</span><br><span class="line">y_pred = gc.predict(X_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;模型的测试集的预测值：<span class="subst">&#123;y_pred&#125;</span>&quot;</span>)</span><br><span class="line">ret = gc.score(X_test,y_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f&quot;最佳模型在测试集上的评分:<span clasLine truncated