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<meta property="og:description" content="Bagging随机森林 Random-Forest 就是Bagging + Decisiontree 1234567891011121314151617181920212223242526272829303132333435import seaborn as snsimport pandas as pdimport numpy as npfrom sklearn.model_selection">
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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 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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="headerlink" title="Bagging"></a>Bagging</h3><h3 id="随机森林"><a href="#随机森林" class="headerlink" title="随机森林"></a>随机森林</h3><blockquote>
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<p><code>Random-Forest</code> 就是<code>Bagging + Decisiontree</code></p>
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</blockquote>
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<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">'titanic'</span>)</span><br><span class="line">missing_age_count = titanic[<span class="string">'age'</span>].isna().<span class="built_in">sum</span>()</span><br><span class="line"><span class="comment"># print(f"缺失的 age 数量: {missing_age_count}")</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">'pclass'</span>,<span class="string">'age'</span>,<span class="string">'sex'</span>]]</span><br><span class="line">y = titanic[<span class="string">'survived'</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">'age'</span>] = X[<span class="string">'age'</span>].fillna(value=X[<span class="string">'age'</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">"records"</span>)</span><br><span class="line">X_test= X_test.to_dict(orient=<span class="string">"records"</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={<span class="string">"n_estimators"</span>:[<span class="number">100</span>,<span class="number">120</span>,<span class="number">300</span>],<span class="string">"max_depth"</span>:[<span class="number">3</span>,<span class="number">7</span>,<span class="number">11</span>]},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"模型的测试集的预测值：<span class="subst">{y_pred}</span>"</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"最佳模型在测试集上的评分:<span class="subst">{ret}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型的参数：<span class="subst">{gc.best_estimator_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"最佳模型在训练集上的评分：<span class="subst">{gc.best_score_}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(X_test.toarray())</span><br></pre></td></tr></table></figure>
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<p><img src="/img/machinelearning/random-forest.png"></p>
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<h3 id="ott案例"><a href="#ott案例" class="headerlink" title="ott案例"></a>ott案例</h3><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><span class="line">36</span><br><span class="line">37</span><br><span class="line">38</span><br><span class="line">39</span><br><span class="line">40</span><br><span class="line">41</span><br><span class="line">42</span><br><span class="line">43</span><br><span class="line">44</span><br><span class="line">45</span><br><span class="line">46</span><br><span class="line">47</span><br><span class="line">48</span><br><span class="line">49</span><br><span class="line">50</span><br><span class="line">51</span><br><span class="line">52</span><br><span class="line">53</span><br><span class="line">54</span><br><span class="line">55</span><br><span class="line">56</span><br><span class="line">57</span><br><span class="line">58</span><br><span class="line">59</span><br><span class="line">60</span><br><span class="line">61</span><br><span class="line">62</span><br><span class="line">63</span><br><span class="line">64</span><br><span class="line">65</span><br><span class="line">66</span><br><span class="line">67</span><br><span class="line">68</span><br><span class="line">69</span><br><span class="line">70</span><br><span class="line">71</span><br><span class="line">72</span><br></pre></td><td class="code"><pre><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">import</span> matplotlib.pyplot <span class="keyword">as</span> plt</span><br><span class="line"><span class="keyword">from</span> imblearn.under_sampling <span class="keyword">import</span> RandomUnderSampler</span><br><span class="line"><span class="keyword">from</span> sklearn.model_selection <span class="keyword">import</span> train_test_split</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> LabelEncoder</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="keyword">from</span> sklearn.metrics <span class="keyword">import</span> log_loss</span><br><span class="line"><span class="keyword">from</span> sklearn.preprocessing <span class="keyword">import</span> OneHotEncoder</span><br><span class="line"><span class="comment"># 1. 获取数据集</span></span><br><span class="line">data = pd.read_csv(<span class="string">'./data/train.csv'</span>)</span><br><span class="line"><span class="comment"># 查看目标值分类</span></span><br><span class="line"><span class="keyword">import</span> seaborn <span class="keyword">as</span> sns</span><br><span class="line">sns.countplot(data=data, x=<span class="string">'target'</span>, hue=<span class="string">'target'</span>, palette=<span class="string">"Set2"</span>, legend=<span class="literal">False</span>) <span class="comment"># 使用 hue='target' 替代 palette</span></span><br><span class="line">plt.show()</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2. 数据集的基本处理</span></span><br><span class="line"><span class="comment"># 2.1 确定特征值、目标值</span></span><br><span class="line">x = data.drop([<span class="string">"id"</span>, <span class="string">"target"</span>], axis=<span class="number">1</span>)</span><br><span class="line">y = data[<span class="string">'target'</span>]</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.2 使用随机欠采样进行平衡</span></span><br><span class="line">undersampler = RandomUnderSampler(sampling_strategy=<span class="string">'auto'</span>, random_state=<span class="number">0</span>)</span><br><span class="line">x_resampled, y_resampled = undersampler.fit_resample(x, y)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 查看欠采样后的类别分布</span></span><br><span class="line"><span class="comment"># print(f"欠采样后训练集中的类别分布：\n{y_train_resampled.value_counts()}")</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.3. 将标签转换为数字</span></span><br><span class="line">le = LabelEncoder()</span><br><span class="line">y_resampled = le.fit_transform(y_resampled)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 2.4. 划分训练集和测试集</span></span><br><span class="line">x_train, x_test, y_train, y_test = train_test_split(x_resampled, y_resampled, test_size=<span class="number">0.2</span>)</span><br><span class="line"></span><br><span class="line"> <span class="comment"># 3. 机器学习</span></span><br><span class="line">rf = RandomForestClassifier(oob_score = <span class="literal">True</span>)</span><br><span class="line">rf.fit(x_train,y_train)</span><br><span class="line">y_pred = rf.predict(x_test)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"预测值:<span class="subst">{y_pred}</span>"</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"评分：<span class="subst">{rf.score(x_test,y_test)}</span>"</span>)</span><br><span class="line"></span><br><span class="line"><span class="comment"># # 4. 模型评估 （解决二分类预测问题）</span></span><br><span class="line"><span class="comment"># import numpy as np</span></span><br><span class="line"><span class="comment"># from sklearn.metrics import log_loss</span></span><br><span class="line"><span class="comment"># # 假设 y_pred_prob 是通过 predict_proba 得到的预测概率</span></span><br><span class="line"><span class="comment"># # 对预测概率进行裁剪，将其限制在 [eps, 1-eps] 范围内</span></span><br><span class="line"><span class="comment"># eps = 1e-15 # 设置一个小的eps值，避免极端值</span></span><br><span class="line"><span class="comment"># y_pred_prob = rf.predict_proba(x_test)</span></span><br><span class="line"><span class="comment"># y_pred_prob = np.clip(y_pred_prob, eps, 1 - eps)</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># # 计算 log_loss</span></span><br><span class="line"><span class="comment"># loss = log_loss(y_test, y_pred_prob, normalize=True)</span></span><br><span class="line"><span class="comment"># print(f"Log Loss: {loss}")</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 4. 模型评估 （解决多分类预测问题）</span></span><br><span class="line"></span><br><span class="line"><span class="comment"># 获取预测的概率</span></span><br><span class="line">y_pred_prob = rf.predict_proba(x_test)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 使用 OneHotEncoder 对 y_test 进行 One-Hot 编码</span></span><br><span class="line">encoder = OneHotEncoder(sparse_output=<span class="literal">False</span>) <span class="comment"># 确保返回的是密集矩阵</span></span><br><span class="line">y_test_one_hot = encoder.fit_transform(y_test.reshape(-<span class="number">1</span>, <span class="number">1</span>))</span><br><span class="line"></span><br><span class="line"><span class="comment"># 对预测概率进行裁剪，将其限制在 [eps, 1-eps] 范围内</span></span><br><span class="line">eps = <span class="number">1e-15</span></span><br><span class="line">y_pred_prob = np.clip(y_pred_prob, eps, <span class="number">1</span> - eps)</span><br><span class="line"></span><br><span class="line"><span class="comment"># 计算 log_loss</span></span><br><span class="line">loss = log_loss(y_test_one_hot, y_pred_prob, normalize=<span class="literal">True</span>)</span><br><span class="line"><span class="built_in">print</span>(<span class="string">f"Log Loss: <span class="subst">{loss}</span>"</span>)</span><br><span class="line"></span><br></pre></td></tr></table></figure>
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