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<h1 id="%E5%86%B3%E7%AD%96%E6%A0%91">决策树<a class="anchor-link" href="#%E5%86%B3%E7%AD%96%E6%A0%91">¶</a></h1>
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<h2 id="%E5%86%B3%E7%AD%96%E6%A0%91%E7%9A%84%E6%9E%84%E5%BB%BA%EF%BC%88%E6%9C%80%E5%A4%A7%E5%86%B3%E7%AD%96%E6%B7%B1%E5%BA%A6=1%EF%BC%89">决策树的构建（最大决策深度=1）<a class="anchor-link" href="#%E5%86%B3%E7%AD%96%E6%A0%91%E7%9A%84%E6%9E%84%E5%BB%BA%EF%BC%88%E6%9C%80%E5%A4%A7%E5%86%B3%E7%AD%96%E6%B7%B1%E5%BA%A6=1%EF%BC%89">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
<span class="c1"># 画图工具</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="kn">from</span> <span class="nn">matplotlib.colors</span> <span class="kn">import</span> <span class="n">ListedColormap</span>
<span class="c1"># 导入 tree 模型和数据集加载工具</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span><span class="p">,</span> <span class="n">datasets</span>
<span class="c1"># 导入拆分工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="n">wine</span><span class="o">=</span><span class="n">datasets</span><span class="o">.</span><span class="n">load_wine</span><span class="p">()</span>
<span class="c1"># 只选取数据集的前2个特征,为了图形方便展示</span>
<span class="n">X</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">y</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># 将数据集拆分</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="c1"># 设定决策树分类器的最大深度为1</span>
<span class="n">clf</span><span class="o">=</span><span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
<span class="c1"># 拟合</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 输出打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 最关键的参数就是 max_depth，就是问问题的数量，只能回答yes / no.</span>
<span class="c1"># 问的问题越多，表示决策树的深度越深。</span>
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<pre>training score: 0.692
testing score: 0.622
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化</span>
<span class="c1"># 定义图像中分区的颜色和散点的颜色</span>
<span class="n">cmap_light</span><span class="o">=</span><span class="n">ListedColormap</span><span class="p">([</span><span class="s2">"#FFAAAA"</span><span class="p">,</span> <span class="s2">"#AAFFAA"</span><span class="p">,</span> <span class="s2">"#AAAAFF"</span><span class="p">])</span>
<span class="n">cmap_bold</span><span class="o">=</span><span class="n">ListedColormap</span><span class="p">([</span><span class="s2">"#FF0000"</span><span class="p">,</span> <span class="s2">"#00FF00"</span><span class="p">,</span> <span class="s2">"#0000FF"</span><span class="p">])</span>
<span class="c1"># 分别用样本的2个特征创建图形和x/y轴</span>
<span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="o">=</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="o">=</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">min</span><span class="p">()</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">X_train</span><span class="p">[:,</span> <span class="mi">1</span><span class="p">]</span><span class="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mi">1</span>
<span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">x_min</span><span class="p">,</span> <span class="n">x_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">),</span>
<span class="n">np</span><span class="o">.</span><span class="n">arange</span><span class="p">(</span><span class="n">y_min</span><span class="p">,</span> <span class="n">y_max</span><span class="p">,</span> <span class="mf">0.02</span><span class="p">))</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="c1"># 给每个分类中的样本分配不同的颜色</span>
<span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 样本散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_bold</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: tree( max_depth = 1)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<blockquote><p>只分2类，分类效果不好，不到 70%。</p>
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<h2 id="max_depth=3">max_depth=3<a class="anchor-link" href="#max_depth=3">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [3]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 尝试加大深度 3</span>
<span class="n">clf2</span><span class="o">=</span><span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="c1"># 拟合</span>
<span class="n">clf2</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 输出打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf2</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf2</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
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<pre>training score: 0.887
testing score: 0.822
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<div class="jp-InputPrompt jp-InputArea-prompt">In [4]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf2</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="c1"># 给每个分类中的样本分配不同的颜色</span>
<span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 样本散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_bold</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: tree( max_depth = 3)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="max_depth=5">max_depth=5<a class="anchor-link" href="#max_depth=5">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [5]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 尝试加大深度 5</span>
<span class="n">clf3</span><span class="o">=</span><span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
<span class="c1"># 拟合</span>
<span class="n">clf3</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 输出打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf3</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">clf3</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 出现过拟合倾向了，就是训练集效果远好于测试集。</span>
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<pre>training score: 0.925
testing score: 0.778
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<div class="jp-InputPrompt jp-InputArea-prompt">In [6]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化</span>
<span class="n">Z</span><span class="o">=</span><span class="n">clf3</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">c_</span><span class="p">[</span><span class="n">xx</span><span class="o">.</span><span class="n">ravel</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">ravel</span><span class="p">()])</span>
<span class="c1"># 给每个分类中的样本分配不同的颜色</span>
<span class="n">Z</span><span class="o">=</span><span class="n">Z</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">figure</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">pcolormesh</span><span class="p">(</span><span class="n">xx</span><span class="p">,</span> <span class="n">yy</span><span class="p">,</span> <span class="n">Z</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_light</span><span class="p">,</span> <span class="n">shading</span><span class="o">=</span><span class="s1">'auto'</span><span class="p">)</span>
<span class="c1"># 样本散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">c</span><span class="o">=</span><span class="n">y</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">cmap_bold</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s2">"k"</span><span class="p">,</span> <span class="n">s</span><span class="o">=</span><span class="mi">20</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlim</span><span class="p">(</span><span class="n">xx</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">xx</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylim</span><span class="p">(</span><span class="n">yy</span><span class="o">.</span><span class="n">min</span><span class="p">(),</span> <span class="n">yy</span><span class="o">.</span><span class="n">max</span><span class="p">())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">title</span><span class="p">(</span><span class="s2">"Classifier: tree( max_depth = 5)"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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<h2 id="%E5%86%B3%E7%AD%96%E6%A0%91%E7%9A%84%E5%8F%AF%E8%A7%86%E5%8C%96%E6%BC%94%E7%A4%BA">决策树的可视化演示<a class="anchor-link" href="#%E5%86%B3%E7%AD%96%E6%A0%91%E7%9A%84%E5%8F%AF%E8%A7%86%E5%8C%96%E6%BC%94%E7%A4%BA">¶</a></h2>
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<blockquote><p>pip3 install graphviz -i <a href="https://pypi.douban.com/simple/">https://pypi.douban.com/simple/</a></p>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [7]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">graphviz</span>
<span class="kn">from</span> <span class="nn">sklearn.tree</span> <span class="kn">import</span> <span class="n">export_graphviz</span>
<span class="c1"># 选择 max_depth=3 的决策树进行可视化</span>
<span class="c1"># 输出到文件</span>
<span class="n">export_graphviz</span><span class="p">(</span><span class="n">clf2</span><span class="p">,</span> <span class="n">out_file</span><span class="o">=</span><span class="s2">"wine.dot"</span><span class="p">,</span> <span class="n">class_names</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">target_names</span><span class="p">,</span>
<span class="n">feature_names</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">feature_names</span><span class="p">[:</span><span class="mi">2</span><span class="p">],</span> <span class="n">impurity</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">filled</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="c1"># 读文件</span>
<span class="k">with</span> <span class="nb">open</span><span class="p">(</span><span class="s2">"wine.dot"</span><span class="p">)</span> <span class="k">as</span> <span class="n">f</span><span class="p">:</span>
<span class="n">dot_graph</span><span class="o">=</span><span class="n">f</span><span class="o">.</span><span class="n">read</span><span class="p">()</span>
<span class="c1"># 可视化</span>
<span class="n">graphviz</span><span class="o">.</span><span class="n">Source</span><span class="p">(</span><span class="n">dot_graph</span><span class="p">)</span>
<span class="c1"># 这种层级关系非常方便向非专业人士解释算法是如何工作的。</span>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[7]:</div>
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viewBox="0.00 0.00 1210.50 373.00" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink">
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<text text-anchor="middle" x="582" y="-349.8" font-family="Helvetica,sans-Serif" font-size="14.00">alcohol <= 12.745</text>
<text text-anchor="middle" x="582" y="-334.8" font-family="Helvetica,sans-Serif" font-size="14.00">samples = 133</text>
<text text-anchor="middle" x="582" y="-319.8" font-family="Helvetica,sans-Serif" font-size="14.00">value = [45, 55, 33]</text>
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<text text-anchor="middle" x="440" y="-245.8" font-family="Helvetica,sans-Serif" font-size="14.00">malic_acid <= 2.96</text>
<text text-anchor="middle" x="440" y="-230.8" font-family="Helvetica,sans-Serif" font-size="14.00">samples = 56</text>
<text text-anchor="middle" x="440" y="-215.8" font-family="Helvetica,sans-Serif" font-size="14.00">value = [0, 47, 9]</text>
<text text-anchor="middle" x="440" y="-200.8" font-family="Helvetica,sans-Serif" font-size="14.00">class = class_1</text>
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<title>8</title>
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<text text-anchor="middle" x="745" y="-245.8" font-family="Helvetica,sans-Serif" font-size="14.00">malic_acid <= 2.375</text>
<text text-anchor="middle" x="745" y="-230.8" font-family="Helvetica,sans-Serif" font-size="14.00">samples = 77</text>
<text text-anchor="middle" x="745" y="-215.8" font-family="Helvetica,sans-Serif" font-size="14.00">value = [45, 8, 24]</text>
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<h2 id="max_depth%E4%B8%8E%E6%89%93%E5%88%86%E6%9B%B2%E7%BA%BF">max_depth与打分曲线<a class="anchor-link" href="#max_depth%E4%B8%8E%E6%89%93%E5%88%86%E6%9B%B2%E7%BA%BF">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [8]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="k">def</span> <span class="nf">getScore</span><span class="p">(</span><span class="n">depth</span><span class="p">):</span>
<span class="c1"># 尝试加大深度 5</span>
<span class="n">clf</span><span class="o">=</span><span class="n">tree</span><span class="o">.</span><span class="n">DecisionTreeClassifier</span><span class="p">(</span><span class="n">max_depth</span><span class="o">=</span><span class="n">depth</span><span class="p">)</span>
<span class="c1"># 拟合</span>
<span class="n">clf</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 输出打分</span>
<span class="k">return</span> <span class="p">[</span><span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="n">clf</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)]</span>
<span class="n">scores</span><span class="o">=</span><span class="p">[]</span>
<span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">10</span><span class="p">):</span>
<span class="n">scores</span><span class="o">.</span><span class="n">append</span><span class="p">(</span> <span class="n">getScore</span><span class="p">(</span><span class="n">i</span><span class="p">)</span> <span class="p">)</span>
<span class="n">scores</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
<span class="n">scores</span>
</pre></div>
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<div class="jp-OutputPrompt jp-OutputArea-prompt">Out[8]:</div>
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<pre>array([[0.69172932, 0.62222222],
[0.82706767, 0.77777778],
[0.88721805, 0.82222222],
[0.90977444, 0.77777778],
[0.92481203, 0.77777778],
[0.94736842, 0.73333333],
[0.96992481, 0.75555556],
[0.9924812 , 0.73333333],
[1. , 0.73333333]])</pre>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [9]:</div>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">scores</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"trainning score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">plot</span><span class="p">(</span><span class="n">scores</span><span class="p">[:,</span><span class="mi">1</span><span class="p">],</span> <span class="n">label</span><span class="o">=</span><span class="s2">"testing score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xlabel</span><span class="p">(</span><span class="s2">"Max_depth"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">ylabel</span><span class="p">(</span><span class="s2">"Score"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">legend</span><span class="p">()</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
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iVLllTqPV9//XWmTJlCQEAAl112WYUlvJcuXcqTTz5JUFAQTZo04e233yYyMpIZM2Ywfvx4iouLiYqKYsGCBTz22GPceuut9OrVi9DQUN56660K3/f555/nrrvuolevXhQWFjJs2DBeeeWVav7mjD/bn3GS+UnO/ITEvUcpVmgdFsJP451JawM72qS1usKr5b5FZDTwHBAIvKaq08rtb4+zclwkcBS4UVWT3X1FQEmv8n5Vvfps72Xlvh1l2/2nTZvGoUOHeO6553wcVfXUx78/f6SqfJdy3O1PSGVbqnNX261VU0bGRZMQ14qLYppZf0It5ZNy3yISCLwEJADJwBoR+URVk8oc9hTwtqq+JSJXAH8Dfu7uO6Wqvb0VX1312Wef8be//Y3CwkLat2/Pm2++6euQTC2UX1jMqt0ZpcNRD2c5k9b6u5PWRsa1ol14qK/DNF7mzSamAcBOVd0NICJzgGuAsgkiDnjAfb4E+NiL8dQLEyZMYMKECb4Ow9RCWbkFLN3mTFpbujWN7LxCGgUFMqxLBA/GdeWKblE2aa2e8WaCiAEOlHmdDAwsd8wGYDxOM9R1QFMRCVfVDCBERBKBQmCaqn5c/g1EZCowFaBdu3YVBqGqdutbC9WVlQ793eHjzkpr8zcfZtXuDAqKlPDGDRnbszUJcdFc2jmCkCCbtFZf+bqT+kHgRRGZDCwDUoAid197VU0RkU7AYhH5TlV3lT1ZVWcAM8Dpgyh/8ZCQEDIyMggPD7ckUYuoKhkZGTZk1gtUle2pJ0rrHW1IPg5Ax4jG3DKkIyPjouljk9aMy5sJIgVoW+Z1rLutlKoexLmDQESaAD9W1Ux3X4r7524RWQr0AU5LEOcSGxtLcnIy6enp1fwRjK+EhIQQGxvr6zDqhKJiZe2+Y8zffJgFW1LZ505a6+1OWhvVI5oLIm3SmvkhbyaINUBnEemIkxgmAjeUPUBEIoCjqloMPIIzogkRaQGcVNU895ghwD+qGkBQUFDpzGBj6pNT+UV8tcPpT1i0NY2jOfk0DAxg8AXhTB3mTFqLbmZ3aObsvJYgVLVQRO4GvsAZ5jpLVTeLyBNAoqp+AgwH/iYiitPEdJd7enfgVREpxpnMN63c6CdjTDkZJ/JYtDWNBUmpfLUjndyCYpqGNOCKblGMjGvFsC4RNLWV1kwVeHUeRE2qaB6EMXXdvoyc0vkJifucSWttwkJIiItmZI9WDOjYkqBAm7Rmzswn8yCMMZ6nqmxMPl46P6HspLW7r+jMyLhoerSxSWvGMyxBGOPnSiatzU86zMKkNA5n5RIYIPTv0II/XRVHQlw0bVvapDXjeZYgjPFDZ5q0dlmXSBLiormiWxQtbNKa8TJLEMb4iUPHT7HQXVSnZNJaRJOGjOvlTFobcqFNWjM1yxKEMT5SdtLa/KRUNrqT1jpFNObWIR0Z2SOa3m1t0prxHUsQxtSgomIlce9Rp5O5zKS1Pu2a85vRXRkZ14oLo2wVPuMfLEEY42Ulk9bmJ6WyuMyktSEXhnP7sAu4snsUUTZpzfghSxDGeFBBUTHbDmezMfk4G5Mz2ZB8nO2p2RQVK81KJq31aMWwLpE0Cbb/fsa/2b9QY6qpuFjZfSSHjcmZbEw+zobkTJIOZpFXWAxAWKMgesWGMaLbBQy+INwmrZlaxxKEMZWgqqRknipNBBsPHGdTynGy8woBCG0YyEVtwvj5oPb0atuci2PDaNcy1CasmVrNEoQxFUjPzittIvrOvUPIyMkHIChQ6N66Gdf0aUOv2OZcHNucC6Oa2GgjU+dYgjD1XlZuAd+VuTPYmJzJweO5AAQIdI5qyhXdokrvDLq2akpwA5uPYOo+SxCmXjmVX0TSoeNscBPBxuTj7D6SU7q/fXgo/Tq05NbYMHrFNqdHm2Y0ts5kU0/Zv3xTZ5WMKNqQnOneIXw/ogggulkwvWKbM75vDL1im9MrNozmoVa+wpgSliBMnbEvI4fEvcdK+w6SDmWR744oah4aRK/Y5lzZPYqeMWFc3La5LZhjzDlYgjC1VnGxsiE5k/lu6eudaScAd0RRTBg3D25f2onctmUjG1FkTBVZgjC1Sl5hEV/vymD+5lQWbUklLTuPwABhUKeW3DiwHZdcGMEFkTaiyBhP8GqCEJHRwHM4S46+pqrTyu1vj7MOdSRwFLhRVZPdfTcDf3AP/YuqvuXNWI3/On6ygCXbnKU0l25LIye/iMYNAxneNYqEuGgu7xpFWKgtpWmMp3ktQYhIIPASkAAkA2tE5JNya0s/Bbytqm+JyBXA34Cfi0hL4FEgHlBgrXvuMW/Fa/xLSuYpFmw+zIItqXyz+yiFxUpk02Cu7h3DyB7RDO4UbqWvjfEyb95BDAB2qupuABGZA1wDlE0QccAD7vMlwMfu81HAAlU96p67ABgNzPZivMaHVJUth7Kd9ZWTDrP5YBYAF0Q2ZsqwTiTERdM7tjkB1nRkTI3xZoKIAQ6UeZ0MDCx3zAZgPE4z1HVAUxEJP8O5Md4L1fhCYVExq0tKXyelknzsFCLQt10LHhnTjYS4aDpFWulrY3zF153UDwIvishkYBmQAhRV9mQRmQpMBWjXrp034jMelpNXyLLtzlKai7elkXmygIYNAhh6YQR3X34hI7pHE9k02NdhGmPwboJIAdqWeR3rbiulqgdx7iAQkSbAj1U1U0RSgOHlzl1a/g1UdQYwAyA+Pl49GLvxoPTsPBZtcZbSXL7zCPmFxTQPDXJKX8dFM7RzpM1WNsYPefN/5Rqgs4h0xEkME4Ebyh4gIhHAUVUtBh7BGdEE8AXwVxFp4b4e6e43tcSu9BNOf8Lmw3x7IBNViG3RiBsHtichLpr+HVrQwEpfG+PXvJYgVLVQRO7G+bAPBGap6mYReQJIVNVPcO4S/iYiitPEdJd77lER+TNOkgF4oqTD2vin4mLl2wOZbn/CYXalO/WNLoppxn0jujCyRzTdWjW1yWrG1CKiWjdaZuLj4zUxMdHXYdQruQVFrNyVwfykwyzckkZ6dh4NAoSBnVoyMq4VV8ZFE9O8ka/DNMachYisVdX4ivZZw6+psi82H+a/61NYui2dk2UmrY3sEc3wLjZpzZi6whKEqbSComIe/99m3lm1n8imwVzbJ4aEuGguuSDc1kcwpg6yBGEq5VhOPr98dx0rd2dw+2Wd+M2oblbvyJg6zhKEOaedadnc9lYihzJzefr6ixnfN9bXIRljaoAlCHNWS7am8avZ3xIcFMjsqYPo177FuU8yxtQJliBMhVSV177aw1/nbSGudTNm3hRPGxuRZEy9YgnC/EBeYRG//88mPlybzJiLWvF/119MaEP7p2JMfWP/681p0rPzuOOdtazdd4x7R3Tm3hGdrYKqMfWUJQhTKulgFlPeTiQjJ4+XbujLuF6tfR2SMcaHLEEYAD7fdJj7319P89AgPrzjEi6KCfN1SMYYH7MEUc+pKi8u3sn/LdhO77bNmfHzfkQ1C/F1WMYYP2AJoh47lV/Ebz7ayP82HGR8nxj+Or6nLeNpjCllCaKeOnw8lylvJ7Lp4HEeHtON24d1skqrxpjTWIKoh9YfyGTq24nk5BUy8+fxXBkX7euQjDF+yBJEPfPf9Sk89OFGopsF88/bhtC1VVNfh2SM8VOWIOqJ4mLlqfnbeHnpLgZ0bMkrN/ajZeOGvg7LGOPHLEHUAyfyCrn//fUsSEpl0oC2PH71RTRsYMt9GmPOzhJEHXfg6EmmvJ3I9tRsHvtRHDdf0sE6o40xleLVr5EiMlpEtonIThF5uIL97URkiYh8KyIbRWSsu72DiJwSkfXu4xVvxllXrd5zlGteWsHBzFO8desAJg/paMnBGFNpXruDEJFA4CUgAUgG1ojIJ6qaVOawPwAfqOp0EYkD5gId3H27VLW3t+Kr695fs58/fLyJti1Cee3meDpFNvF1SMaYWsabTUwDgJ2quhtAROYA1wBlE4QCzdznYcBBL8ZTLxQWFfPXuVuZtWIPQztH8OKkvrZGtDGmWryZIGKAA2VeJwMDyx3zGDBfRO4BGgNXltnXUUS+BbKAP6jqV+XfQESmAlMB2rVr57nIa6njpwq4Z/a3LNuezi1DOvD7sd1pEGid0caY6vH1p8ck4E1VjQXGAv8UkQDgENBOVfsADwDviUiz8ier6gxVjVfV+MjIyBoN3N/sTj/BdS+vYOWuI0wb35NHf9TDkoMx5rx48w4iBWhb5nWsu62s24DRAKq6UkRCgAhVTQPy3O1rRWQX0AVI9GK8tdZXO9K56911NAgM4J3bBjKwU7ivQzLG1AHe/Iq5BugsIh1FpCEwEfik3DH7gREAItIdCAHSRSTS7eRGRDoBnYHdXoy1VlJV3vp6L5PfWEPrsEb8964hlhyMMR7jtTsIVS0UkbuBL4BAYJaqbhaRJ4BEVf0E+DUwU0Tux+mwnqyqKiLDgCdEpAAoBu5Q1aPeirU2Kigq5tFPNvPeN/u5snsUz07sQ5Ngm9ZijPEcUVVfx+AR8fHxmphYP1qgjubkc+c7a/lmz1HuHH4BD43sasuCGmOqRUTWqmp8RfvsK2ctsz01m9veWkNqVh7PTLiY6/rE+jokY0wdZQmiFlm0JZV756ynUcNA3p86iD7tWvg6JGNMHWYJohZQVWYs2820z7fSo00zZt4UT+uwRr4OyxhTx1U6QYhII5y5Cdu8GI8pJ7egiN/9+zv+/W0K43q15qmfXEyjhrYsqDHG+yo1zFVEfgSsBz53X/cWkfJDVo2HpWXnMmnmKv79bQoPJHThxUl9LDkYY2pMZe8gHsOprbQUQFXXi0hHL8VkgOzcAsa//DUZJ/KZ/rO+jOnZ2tchGWPqmcomiAJVPV6uVHTdGB/rp2Z+tYfkY6f44PbBDOjY0tfhGGPqocrOpN4sIjcAgSLSWUReAL72Ylz1Wnp2Hq99tZtxPVv7X3JQhR0L4Xj5qinGmLqmsgniHqAHTn2k94DjwH1eiqnee2HxDvIKi3lwVFdfh3K6tC3w5jh498fwYn9Y8RwUFfg6KmOMl5yzicmtifSZql4O/N77IdVv+zJyeO+b/Uzs35aOEY19HY4jPwe+/AesfBEaNoHR02DPMljwJ1g/G656Gtpf4usojTEeds47CFUtAopFJKwG4qn3npq/naDAAO4d0dnXoTi2fgYvDYQVz0KviXDPWhh0J0yaDRNnQ/4JeGMM/OdOyDni62iNMR5U2U7qE8B3IrIAyCnZqKq/8kpU9dSmlOP8b8NB7r78QqKahfg2mGP7YN5vYfs8iIqDWz6H9oNPP6bbWOh0GSx7Er5+AbbNhSsfhb6TIcDWojCmtqtsgvi3+zBe9PfPt9IiNIipl3XyXRCF+fD187DsKZAAGPkXGHgHBJ5h2dKGjeHKx5y7i7kPwqf3w7fvOs1OrS+u0dCNMZ5VqQShqm+5azp0cTdtU1XrnfSg5TuO8NWOI/xhXHeahfhoDendXzof8ke2Q/erYfTfIKySxQCjusHN/4ONH8D838OM4TBgKlz+ewj5wWKAxphaoLIzqYcDO4CXgJeB7e6aDcYDiouVv3++lZjmjfj54PY1H0B2Knw0Bd6+Gory4WcfwoR/Vj45lBCBiyfA3YkQfyt886oz2um7D53hscaYWqWyDcX/B4xU1ctUdRgwCnjGe2HVL3M3HeK7lOM8kNCF4AY1WEqjuAhWz3Q+xJM+hmG/gV+ugs4J53fdRs1h3P/BlMXQtBV8dBv881o4stMDQRtjakplE0RQ2SJ9qrod8FE7SN1SUFTMk19so1urplzbJ6bm3jhlLcy8wmlSiukDd66EK34PQR6sEhvT10kSY5+ClG9h+mBY/P+g4JTn3sMY4zWVTRCJIvKaiAx3HzOBcy7fJiKjRWSbiOwUkYcr2N9ORJaIyLcislFExpbZ94h73jYRGVX5H6l2mbN6P/syTvLb0d0IrIlV4U4dg08fgJkjIPsw/GQW/PxjiLjQO+8XEAgDpsDda6DHdbDsH/DyINixwDvvZ4zxmEotOSoiwcBdwKXupq+Al1U17yznBALbgQQgGVgDTFLVpDLHzAC+VdXpIhIHzFXVDu7z2TgFAtsAC4Eu7pyMCtXGJUdz8gq57MmldIpszPtTB1Gu1pVnqcLG92H+H+BkBgy4HS7/Xc13IO9ZBp/92u0I/5Ez6a6qfR3GGI/xxJKjDYDnVPVp94KBQPA5zhkA7FTV3e45c4BrgKQyxyhQ8gkVBhx0n18DzHET0B4R2eleb2Ul460VXl++hyMn8phxUz/vJoe0rc6H8r7lENsfbvw3tO7lvfc7m47D4I4VsPIF+PJJ2DkAhj/sTL4701BaY4xPVLaJaRFQtnG6Ec63+rOJAQ6UeZ3sbivrMeBGEUkG5uLUfKrsuYjIVBFJFJHE9PT0c/0MfiXjRB4zlu1mVI9o+npr6dD8HFj4GLwyBFI3wVXPwq3zfZccSjRoCEN/DXd94ySMBX+EV4fBvjqV/42p9SqbIEJU9UTJC/d5qAfefxLwpqrGAmOBf4pIpafgquoMVY1X1fjIyEgPhFNzXlyyk5P5hTw0qpt33mDrXHhpECx/BnpNcEpkxN/iXzOcW7SHG+Y4JTvysuGN0fDxXVaywxg/UdlPixwR6VvyQkTigXMNRUkB2pZ5HetuK+s24AMAVV0JhAARlTy31jpw9CTvrNrH9fFtuTCqiWcvfmwfvDcR5kyC4CZwyzy49mVoHOHZ9/GkbmOdu4lL74eNc+CFfpD4BhQX+zoyY+q1yiaI+4B/ichXIvIVMAe4+xznrAE6i0hHdxb2RKD8MqX7gREAItIdJ0Gku8dNFJFgd+W6zsDqSsbq955esJ0AEe67ssu5D66swnz46mmnsN6eZZDwZ7h9We2pslpSsuOOFRB9EXx6H7yeAIc2+DoyY+qtsyYIEekvIq1UdQ3QDXgfKMBZm3rP2c5V1UKcJPIFsAX4QFU3i8gTInK1e9ivgSkisgFn1NJkdWzGubNIct/rrrONYKpNkg5m8fH6FG4Z0pFWYR4qyLdnmdPPsOhx6Hwl3L0ahvyqdnb6RnWDyZ/Cda9C5j6nZMe8hyE3y9eRGVPvnHWYq4isA65U1aNuaY05OB3JvYHuqvqTGomyEmrLMNfJb6zm2/2ZLHvocsJCz/MD/ESaM2x14/vQvL0zIa3LSM8E6g9OHYPFf4E1r0OTaBj9V+gx3inpYYzxiLMNcz1XE1Ogqh51n08AZqjqR6r6R8BLM6vqrpW7Mli6LZ1fDr/g/JJDSYmMF+Jh83+cEhl3fVO3kgNAoxZuyY5FTsmOD2+Ff15nJTuMqSHnTBAiUjJXYgSwuMy+ys6hMICqMu3zrbQOC+HmSzpU/0Ip6+C1EU6JjDa94c6vPV8iw9/E9CtTsmOdlewwpoacK0HMBr4Ukf/ijFr6CkBELsRZl9pU0hebD7PhQCb3X9mFkKBqFOQ7lelMdpt5BWQdhB+/Djf9FyL8ZOU5b7OSHcbUuHOW2hCRQUBrYL6q5rjbugBNVHWd90OsHH/ugygsKmbkM8sIDBDm3TuUBoFVmIug+v0aCycz3DUWfgch9XwF2NNKdlztluyowWKHxtQR51VqQ1VXVbBtuycCqy8+SExm95EcZt4UX7XkkL7N+RDc+xXExMONH9kqbSV+ULJjEVz+CHQbB/hhJ3ZYWwi0VllTu1SqWF9t4K93EKfyi7jsySW0axnKv+4YXPmaS9mp8EJfCGjgzA/oe7N/zYL2J8f2uutnf+7rSM4svLPT4d7pMl9HYsxpPFGsz1TTrBV7SMvO46Wf9a1aQb5VL0PBSbhrdf3pZ6iuFh3ghvdh73LIPHDOw2tcQQ58/YKzYl/Pn8LI/wdNo30dlTHnZAnCi47l5PPKl7u4snsU/Tu0rPyJucchcRbEXWPJoSo6XHruY3yl98+culjLn4HtX8AVf4T+tzmd78b4KWuz8KKXl+4kJ68aBfkSZ0FeFgy5zytxGR8IauQMLrhzpbPS3ryHYOblkLzW15EZc0aWILwkJfMUb63cx/i+sXRt1bTyJxbkwsqX4YIrnHkOpm6JuNBZwe8nbzj9TK+NgE/vd2aNG+NnLEF4yTMLnIFe9ydUsSDfhtmQk2Z3D3WZCFw03pnTMehOWPumMyt+/WxnWLMxfsIShBdsO5zNv9clc/Pg9sQ0r8IM5+IiWPEctOnrDOM0dVtIMxj9N5j6JbTsCB/fAW+Og7Qtvo7MGMAShFc8+cVWGgc34JfDq1iuKum/cGyPsy6CFaSrP1r3clb6+9FzkLoZXrkUFjzqrAhojA9ZgvCwNXuPsnBLGndcdgEtGjes/ImqsOJZCL/Qnexl6pWAAOg32Vn5r9dE59/CSwNhy6fW7GR8xhKEB6kq0+ZtJappMLcO6Vi1k3cvcRbHGXKvDX2szxpHwLUvwS2fQ3BTeP9nMHuiMxnQmBpmCcKDFiSlsnbfMe67sguNGlbxQ375M9C0tbN+tDHtBzsrAo78C+z5yllffNlTUJjn68hMPWIJwkMKi4p58ottdIpozPXxsVU7OWWtU3xu0C+hQbB3AjS1T2AQXHKPs0Jg5wRY/GeYPgR2f+nryLwn66Azqmv9bMjJ8HU09Z5XZ1KLyGjgOSAQeE1Vp5Xb/wxwufsyFIhS1ebuviLgO3ffflW9Gj/273Up7Eg7wfSf9a1aQT6A5c861Vn7TfZGaKa2C4uFCf90SpvPfdAt2XG9c3dR20t2qELqJtg6F7bNhUPrv98nAdB2EHQdA13HOnNITI3yWrE+EQkEtgMJQDKwBpikqklnOP4eoI+q3uq+PqGqTSr7fr4s1pdbUMTlTy0lqlkIH//ykqrVXDqyA17sD0N/DSP+6L0gTd1QcAq+etrpxG4QUjtLdhTmw77lsG2e8zh+ABCI7Q/dxkKXMVCY6ySMbXPhsPs9Mbyzs7/rWOfY2vQz+zFfFesbAOxU1d1uEHOAa4AKEwQwCXjUi/F4zVtf7+XQ8VyemdC7askB4OvnnWalgXd4JzhTtwQ1clYQ7DUB5v7aKdmx/h246hln5T1/deoY7FjofODvXOiUkmnQyKkYcNlvocsoaBJ1+jltejvlSTIPuMlkrlNlYMVzEBoOXUY7dxcXXAENG/vkx6rrvJkgYoCypTWTgYEVHSgi7YGOnL6kaYiIJAKFwDRV/biC86YCUwHatWvnmair6PjJAl5euovhXSMZ1Cm8aidnHXTaWvvdDE0ivROgqZtKSnZs/jd8/juYOQLib3XuQhu18HV0jmN7nQ/2rZ/Bvq9Bi6BxFPS41rkL6DS8ckvlNm8LA6c6j9zjztof2+bC1k9h/bsQGOxcq+sY59G0lXd/rnrEX6q5TgQ+VNWiMtvaq2qKiHQCFovId6q6q+xJqjoDmAFOE1PNhfu96V/uIiu3gN9UtSAfOCW9tdjpiDSmqkTgoh/DhQmw5K+w+lVnsuXIv8DFE2t+smVxMRz8FrZ95iSGNLexILK7M3y761jnLud81jUJCXPKlFw0HooKYP/K75PQji/g0/uc9yjpt4iKs0mn58GbCSIFaFvmday7rSITgbvKblDVFPfP3SKyFOgD7Prhqb5z+Hgub6zYw7W9Y4hr06xqJ586BolvOOsrt+jglfhMPRHSDMZMg96T4NMHnJId377jLFAUVY0vLlVRcMoZVbVtrrNg04lUkEBofwmM+qvzQd2yk3feOzDIKUnTcZjzXmlb3H6LebD4L86jeXsnUXQd48QUGOSdWOoobyaINUBnEemIkxgmAjeUP0hEugEtgJVltrUATqpqnohEAEOAf3gx1mp5duF2VOGBqhbkA1jzOuSfgEvv83hcpp5qfTHctgDWvQULH4NXhsDgu+Gy33i2jf5EuvNtfds82LXYWdiqYVPofKXzYXzhlRBahfVPPEEEouOcx7AHIfuwk7C2zYO1b8A30yE4zBku3HWM82d9X9e9EryWIFS1UETuBr7AGeY6S1U3i8gTQKKqfuIeOhGYo6cPp+oOvCoixThzNaadafSTr+xMO8EHiQe4+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<h1 id="%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97">随机森林<a class="anchor-link" href="#%E9%9A%8F%E6%9C%BA%E6%A3%AE%E6%9E%97">¶</a></h1>
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<li>随机森林也称为 随机决策森林，是一种集合学习方法，既可用于分类，也可用于回归。</li>
<li>集合算法，包括 随机森林(Random Forests)和梯度上升决策树(Gradient Boosted Decision Tree, GBDT)。</li>
</ul>
<p>随机森林是把不同的几棵决策树打包到一起，每棵树的参数都不相同，然后我们取每棵树预测结果的平均值。</p>
<ul>
<li>这样既可以保留决策树们的工作成效，又可以降低过拟合的风险。</li>
<li>可以用数学公式推导。略。</li>
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<h2 id="%E7%BB%A7%E7%BB%AD%E4%BD%BF%E7%94%A8-wine-%E6%95%B0%E6%8D%AE%E9%9B%86">继续使用 wine 数据集<a class="anchor-link" href="#%E7%BB%A7%E7%BB%AD%E4%BD%BF%E7%94%A8-wine-%E6%95%B0%E6%8D%AE%E9%9B%86">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入随机森林分类器</span>
<span class="kn">from</span> <span class="nn">sklearn.ensemble</span> <span class="kn">import</span> <span class="n">RandomForestClassifier</span>
<span class="c1"># 导入拆分工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">train_test_split</span>
<span class="kn">from</span> <span class="nn">sklearn</span> <span class="kn">import</span> <span class="n">tree</span><span class="p">,</span> <span class="n">datasets</span>
<span class="n">wine</span><span class="o">=</span><span class="n">datasets</span><span class="o">.</span><span class="n">load_wine</span><span class="p">()</span>
<span class="c1"># 只选取数据集的前2个特征,为了图形方便展示</span>
<span class="n">X</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">[:,</span> <span class="p">:</span><span class="mi">2</span><span class="p">]</span>
<span class="n">y</span><span class="o">=</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span>
<span class="c1"># 将数据集拆分</span>
<span class="n">X_train</span><span class="p">,</span> <span class="n">X_test</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">y_test</span> <span class="o">=</span> <span class="n">train_test_split</span><span class="p">(</span><span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">8</span><span class="p">)</span>
<span class="c1"># 设定随机森林有6棵树</span>
<span class="n">forest</span><span class="o">=</span><span class="n">RandomForestClassifier</span><span class="p">(</span><span class="n">n_estimators</span><span class="o">=</span><span class="mi">6</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
<span class="c1"># 拟合</span>
<span class="n">forest</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">)</span>
<span class="c1"># 输出打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"training score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">forest</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">))</span> <span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"testing score: </span><span class="si">{:.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">forest</span><span class="o">.</span><span class="n">score</span><span class="p">(</span><span class="n">X_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">))</span> <span class="p">)</span>
<span class="c1"># 测试集的结果比训练集明显差，已经过拟合了。</span>
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<pre>training score: 0.977
testing score: 0.778
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<div class=" highlight hl-ipython3"><pre><span></span><span class="n">help</span><span class="p">(</span><span class="n">RandomForestClassifier</span><span class="p">)</span>
<span class="c1"># bootstrap=True 是一个重要的参数，也是默认值。</span>
<span class="c1"># 每棵树都是随机的样本，而每棵树也会选择不同的特征，保证每棵树都是不同的。</span>
<span class="c1"># max_feature 也是一个重要的参数，默认是 auto=sqrt(特征数量)。太少则每棵树差异太大，太大则每棵树基本都一样。</span>
<span class="c1"># n_estimators 是决策树的数量。这些树的概率投票决定着随机森林的输出。</span>
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<pre style="max-height:300px; background: #eee;">Help on class RandomForestClassifier in module sklearn.ensemble._forest:
class RandomForestClassifier(ForestClassifier)
| RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='auto', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)
|
| A random forest classifier.
|
| A random forest is a meta estimator that fits a number of decision tree
| classifiers on various sub-samples of the dataset and uses averaging to
| improve the predictive accuracy and control over-fitting.
| The sub-sample size is controlled with the `max_samples` parameter if
| `bootstrap=True` (default), otherwise the whole dataset is used to build
| each tree.
|
| Read more in the :ref:`User Guide <forest>`.
|
| Parameters
| ----------
| n_estimators : int, default=100
| The number of trees in the forest.
|
| .. versionchanged:: 0.22
| The default value of ``n_estimators`` changed from 10 to 100
| in 0.22.
|
| criterion : {"gini", "entropy"}, default="gini"
| The function to measure the quality of a split. Supported criteria are
| "gini" for the Gini impurity and "entropy" for the information gain.
| Note: this parameter is tree-specific.
|
| max_depth : int, default=None
| The maximum depth of the tree. If None, then nodes are expanded until
| all leaves are pure or until all leaves contain less than
| min_samples_split samples.
|
| min_samples_split : int or float, default=2
| The minimum number of samples required to split an internal node:
|
| - If int, then consider `min_samples_split` as the minimum number.
| - If float, then `min_samples_split` is a fraction and
| `ceil(min_samples_split * n_samples)` are the minimum
| number of samples for each split.
|
| .. versionchanged:: 0.18
| Added float values for fractions.
|
| min_samples_leaf : int or float, default=1
| The minimum number of samples required to be at a leaf node.
| A split point at any depth will only be considered if it leaves at
| least ``min_samples_leaf`` training samples in each of the left and
| right branches. This may have the effect of smoothing the model,
| especially in regression.
|
| - If int, then consider `min_samples_leaf` as the minimum number.
| - If float, then `min_samples_leaf` is a fraction and
| `ceil(min_samples_leaf * n_samples)` are the minimum
| number of samples for each node.
|
| .. versionchanged:: 0.18
| Added float values for fractions.
|
| min_weight_fraction_leaf : float, default=0.0
| The minimum weighted fraction of the sum total of weights (of all
| the input samples) required to be at a leaf node. Samples have
| equal weight when sample_weight is not provided.
|
| max_features : {"auto", "sqrt", "log2"}, int or float, default="auto"
| The number of features to consider when looking for the best split:
|
| - If int, then consider `max_features` features at each split.
| - If float, then `max_features` is a fraction and
| `round(max_features * n_features)` features are considered at each
| split.
| - If "auto", then `max_features=sqrt(n_features)`.
| - If "sqrt", then `max_features=sqrt(n_features)` (same as "auto").
| - If "log2", then `max_features=log2(n_features)`.
| - If None, then `max_features=n_features`.
|
| Note: the search for a split does not stop until at least one
| valid partition of the node samples is found, even if it requires to
| effectively inspect more than ``max_features`` features.
|
| max_leaf_nodes : int, default=None
| Grow trees with ``max_leaf_nodes`` in best-first fashion.
| Best nodes are defined as relative reduction in impurity.
| If None then unlimited number of leaf nodes.
|
| min_impurity_decrease : float, default=0.0
| A node will be split if this split induces a decrease of the impurity
| greater than or equal to this value.
|
| The weighted impurity decrease equation is the following::
|
| N_t / N * (impurity - N_t_R / N_t * right_impurity
| - N_t_L / N_t * left_impurity)
|
| where ``N`` is the total number of samples, ``N_t`` is the number of
| samples at the current node, ``N_t_L`` is the number of samples in the
| left child, and ``N_t_R`` is the number of samples in the right child.
|