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<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
<p><img src="/img/machinelearning/random-forest.png"></p>
<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">&#x27;./data/train.csv&#x27;</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">&#x27;target&#x27;</span>, hue=<span class="string">&#x27;target&#x27;</span>, palette=<span class="string">&quot;Set2&quot;</span>, legend=<span class="literal">False</span>) <span class="comment"># 使用 hue=&#x27;target&#x27; 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">&quot;id&quot;</span>, <span class="string">&quot;target&quot;</span>], axis=<span class="number">1</span>)</span><br><span class="line">y = data[<span class="string">&#x27;target&#x27;</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">Line truncated
<p><img src="/img/machinelearning/ott.png"></p>
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