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<!DOCTYPE html>
<html>
<head><meta charset="utf-8" />
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<title>sklearn_CrossValidation</title><script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script>
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<p>chapter 11 模型评估与优化</p>
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<h1 id="%E4%BD%BF%E7%94%A8%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E8%BF%9B%E8%A1%8C%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BC%B0">使用交叉验证进行模型评估<a class="anchor-link" href="#%E4%BD%BF%E7%94%A8%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E8%BF%9B%E8%A1%8C%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BC%B0">¶</a></h1>
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<p>除了之前的 train_test_split 函数外，还有更自动化的拆分测试工具。就是交叉验证法(Cross Validation)。</p>
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<p>K折叠交叉验证法(k-fold cross validation):</p>
<ul>
<li>把数据随机拆分成k份，对k-1份训练，使用1份测试。</li>
<li>循环k次，则每份都做过测试集。</li>
<li>对外输出5次测试集的打分平均数。</li>
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<h2 id="%E9%9A%8F%E6%9C%BA%E6%8B%86%E5%88%86%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E6%B3%95-shuffle-split-cross-validation">随机拆分交叉验证法 shuffle-split cross validation<a class="anchor-link" href="#%E9%9A%8F%E6%9C%BA%E6%8B%86%E5%88%86%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E6%B3%95-shuffle-split-cross-validation">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入红酒数据集</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>
<span class="c1"># 导入交叉验证工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="c1"># 导入用于分类的支持向量机模型</span>
<span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="c1"># 载入数据</span>
<span class="n">wine</span> <span class="o">=</span> <span class="n">load_wine</span><span class="p">()</span>
<span class="c1"># 设置 SVC 的核函数为 linear</span>
<span class="n">svc</span> <span class="o">=</span> <span class="n">SVC</span><span class="p">(</span><span class="n">kernel</span><span class="o">=</span><span class="s1">'linear'</span><span class="p">)</span>
<span class="c1"># 使用交叉验证法对SVC进行评分</span>
<span class="n">scores</span><span class="o">=</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">svc</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span> <span class="c1">#默认分成5份</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">Mean score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="p">)</span>
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<pre>[0.88888889 0.94444444 0.97222222 1. 1. ]
Mean score:0.961
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># sklearn对分类模型，默认使用的是 分层k交叉验证法。</span>
<span class="nb">print</span><span class="p">(</span><span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">)</span> <span class="c1">#可见酒分了3类。</span>
<span class="c1"># 所谓的分层，就是如果目标是80%男，20%女，则拆分数据后每一份中男女也是这个比例。</span>
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<pre>[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
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1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2]
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<h2 id="%E9%9A%8F%E6%9C%BA%E6%8B%86%E5%88%86%E6%B3%95(shuffle-split-corss-validation)">随机拆分法(shuffle-split corss-validation)<a class="anchor-link" href="#%E9%9A%8F%E6%9C%BA%E6%8B%86%E5%88%86%E6%B3%95(shuffle-split-corss-validation)">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 原理：先从数据中随机抽取一部分作为训练集，再从其余部分随机抽取一部分作为测试集，进行评分后再迭代，</span>
<span class="c1"># 直到达到我们希望的迭代次数。</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">ShuffleSplit</span>
<span class="c1"># 设置拆分为10份</span>
<span class="n">shuffle_split</span> <span class="o">=</span> <span class="n">ShuffleSplit</span><span class="p">(</span><span class="n">train_size</span><span class="o">=</span><span class="mf">0.7</span><span class="p">,</span> <span class="n">test_size</span><span class="o">=</span><span class="mf">0.2</span><span class="p">,</span> <span class="n">n_splits</span><span class="o">=</span><span class="mi">10</span><span class="p">)</span>
<span class="c1"># 交叉验证</span>
<span class="n">scores</span><span class="o">=</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">svc</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">shuffle_split</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">Mean score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="p">)</span>
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<pre>[0.91666667 0.97222222 0.94444444 0.97222222 0.97222222 0.97222222
0.91666667 0.91666667 0.97222222 1. ]
Mean score:0.956
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<h2 id="%E6%8C%A8%E4%B8%AA%E5%84%BF%E8%AF%95%E8%AF%95%E6%B3%95(-leave-one-out-)">挨个儿试试法( leave-one-out )<a class="anchor-link" href="#%E6%8C%A8%E4%B8%AA%E5%84%BF%E8%AF%95%E8%AF%95%E6%B3%95(-leave-one-out-)">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 原理：每次取一个作为测试集，其他作为训练集。缺点是耗时，特别是对大数据集。</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">LeaveOneOut</span>
<span class="n">loo</span> <span class="o">=</span> <span class="n">LeaveOneOut</span><span class="p">()</span>
<span class="c1"># 交叉验证</span>
<span class="n">scores</span><span class="o">=</span><span class="n">cross_val_score</span><span class="p">(</span><span class="n">svc</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="n">loo</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">scores</span><span class="p">,</span> <span class="s2">"</span><span class="se">\n</span><span class="s2">Mean score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">scores</span><span class="o">.</span><span class="n">mean</span><span class="p">())</span> <span class="p">)</span>
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<pre>[1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1.
1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 0. 1. 1. 1. 0. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.
1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
Mean score:0.955
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<h1 id="%E4%BD%BF%E7%94%A8%E7%BD%91%E7%BB%9C%E6%90%9C%E7%B4%A2%E4%BC%98%E5%8C%96%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0">使用网络搜索优化模型参数<a class="anchor-link" href="#%E4%BD%BF%E7%94%A8%E7%BD%91%E7%BB%9C%E6%90%9C%E7%B4%A2%E4%BC%98%E5%8C%96%E6%A8%A1%E5%9E%8B%E5%8F%82%E6%95%B0">¶</a></h1>
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<p>除了逐个尝试看打分外，还可以使用网络搜索法一次性找到更优的参数设置。</p>
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<h2 id="%E7%AE%80%E5%8D%95%E7%BD%91%E7%BB%9C%E6%90%9C%E7%B4%A2">简单网络搜索<a class="anchor-link" href="#%E7%AE%80%E5%8D%95%E7%BD%91%E7%BB%9C%E6%90%9C%E7%B4%A2">¶</a></h2>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 目的: 尝试2个参数的组合 max_iter=100,1000,5000,10000, alpha=10,1,0.1,0.01;</span>
<span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="kn">import</span> <span class="n">load_wine</span>
<span class="n">wine</span> <span class="o">=</span> <span class="n">load_wine</span><span class="p">()</span>
<span class="c1"># 导入套索回归模型</span>
<span class="kn">from</span> <span class="nn">sklearn.linear_model</span> <span class="kn">import</span> <span class="n">Lasso</span>
<span class="c1"># 导入数据集拆分工具</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="c1"># 将数据集拆分为训练集与测试集</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">wine</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">38</span><span class="p">)</span>
<span class="c1"># 设置初试分数0</span>
<span class="n">best_score</span><span class="o">=</span><span class="mi">0</span>
<span class="c1"># 设置 alpha 参数4个遍历</span>
<span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</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="c1">#设置最大迭代次数, 4个遍历</span>
<span class="k">for</span> <span class="n">max_iter</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">10000</span><span class="p">]:</span>
<span class="n">lasso</span><span class="o">=</span><span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="n">max_iter</span><span class="p">)</span>
<span class="n">lasso</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="n">score</span><span class="o">=</span><span class="n">lasso</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="c1"># print(alpha, max_iter, score)</span>
<span class="c1"># 最高分数</span>
<span class="k">if</span> <span class="n">score</span> <span class="o">></span> <span class="n">best_score</span><span class="p">:</span>
<span class="n">best_score</span><span class="o">=</span><span class="n">score</span>
<span class="n">best_parameters</span><span class="o">=</span><span class="p">{</span><span class="s2">"alpha"</span><span class="p">:</span><span class="n">alpha</span><span class="p">,</span> <span class="s2">"max_iter"</span><span class="p">:</span><span class="n">max_iter</span><span class="p">}</span>
<span class="c1"># 输出</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best_score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_score</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best_parameters:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_parameters</span><span class="p">))</span>
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<pre>best_score:0.889
best_parameters:{'alpha': 0.01, 'max_iter': 100}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 上述做法是有缺陷的，因为16次评分使用的是同一个训练集和测试集。不能反映出新数据及的情况。</span>
<span class="c1"># 比如换一个拆分方法，打分也会变 random_state=0</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">wine</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">wine</span><span class="o">.</span><span class="n">target</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
<span class="n">best_score</span><span class="o">=</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</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="k">for</span> <span class="n">max_iter</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">10000</span><span class="p">]:</span>
<span class="n">lasso</span><span class="o">=</span><span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="n">max_iter</span><span class="p">)</span>
<span class="n">lasso</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="n">score</span><span class="o">=</span><span class="n">lasso</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="k">if</span> <span class="n">score</span> <span class="o">></span> <span class="n">best_score</span><span class="p">:</span>
<span class="n">best_score</span><span class="o">=</span><span class="n">score</span>
<span class="n">best_parameters</span><span class="o">=</span><span class="p">{</span><span class="s2">"alpha"</span><span class="p">:</span><span class="n">alpha</span><span class="p">,</span> <span class="s2">"max_iter"</span><span class="p">:</span><span class="n">max_iter</span><span class="p">}</span>
<span class="c1"># 输出</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best_score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_score</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best_parameters:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_parameters</span><span class="p">))</span>
<span class="c1"># 改变拆分方法后，最佳参数变为 alpha=0.1, 最佳打分也下降了</span>
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<pre>best_score:0.830
best_parameters:{'alpha': 0.1, 'max_iter': 100}
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<h2 id="%E4%B8%8E%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E7%BB%93%E5%90%88%E7%9A%84%E7%BD%91%E7%BB%9C%E6%90%9C%E7%B4%A2">与交叉验证结合的网络搜索<a class="anchor-link" href="#%E4%B8%8E%E4%BA%A4%E5%8F%89%E9%AA%8C%E8%AF%81%E7%BB%93%E5%90%88%E7%9A%84%E7%BD%91%E7%BB%9C%E6%90%9C%E7%B4%A2">¶</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="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">cross_val_score</span>
<span class="n">best_score</span><span class="o">=</span><span class="mi">0</span>
<span class="k">for</span> <span class="n">alpha</span> <span class="ow">in</span> <span class="p">[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</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="k">for</span> <span class="n">max_iter</span> <span class="ow">in</span> <span class="p">[</span><span class="mi">100</span><span class="p">,</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">5000</span><span class="p">,</span> <span class="mi">10000</span><span class="p">]:</span>
<span class="n">lasso</span><span class="o">=</span><span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="n">alpha</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="n">max_iter</span><span class="p">)</span>
<span class="n">scores</span> <span class="o">=</span> <span class="n">cross_val_score</span><span class="p">(</span><span class="n">lasso</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">cv</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="n">score</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">mean</span><span class="p">(</span><span class="n">scores</span><span class="p">)</span>
<span class="k">if</span> <span class="n">score</span> <span class="o">></span> <span class="n">best_score</span><span class="p">:</span>
<span class="n">best_score</span><span class="o">=</span><span class="n">score</span>
<span class="n">best_parameters</span><span class="o">=</span><span class="p">{</span><span class="s2">"alpha"</span><span class="p">:</span><span class="n">alpha</span><span class="p">,</span> <span class="s2">"max_iter"</span><span class="p">:</span><span class="n">max_iter</span><span class="p">}</span>
<span class="c1"># 输出</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best_score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_score</span><span class="p">))</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"best_parameters:</span><span class="si">{}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">best_parameters</span><span class="p">))</span>
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<pre>best_score:0.865
best_parameters:{'alpha': 0.01, 'max_iter': 100}
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 再试试验证集上，使用最优参数</span>
<span class="n">lasso</span><span class="o">=</span><span class="n">Lasso</span><span class="p">(</span><span class="n">alpha</span><span class="o">=</span><span class="mf">0.01</span><span class="p">,</span> <span class="n">max_iter</span><span class="o">=</span><span class="mi">100</span><span class="p">)</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="nb">print</span><span class="p">(</span><span class="s2">"test score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">lasso</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="c1"># 这个打分有点低。因为lasso会舍弃特征，而我们的特征本来就不多，说明舍弃的列不那么冗余。</span>
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<pre>test score:0.819
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 使用内置的 GridSearchCV 简化上述循环</span>
<span class="c1">#导入网络搜索工具</span>
<span class="kn">from</span> <span class="nn">sklearn.model_selection</span> <span class="kn">import</span> <span class="n">GridSearchCV</span>
<span class="n">params</span> <span class="o">=</span> <span class="p">{</span><span class="s1">'alpha'</span><span class="p">:[</span><span class="mf">0.01</span><span class="p">,</span> <span class="mf">0.1</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="s1">'max_iter'</span><span class="p">:[</span><span class="mi">100</span><span class="p">,</span><span class="mi">1000</span><span class="p">,</span><span class="mi">5000</span><span class="p">,</span><span class="mi">10000</span><span class="p">]}</span>
<span class="c1"># 定义模型和参数</span>
<span class="n">grid_search</span> <span class="o">=</span> <span class="n">GridSearchCV</span><span class="p">(</span><span class="n">lasso</span><span class="p">,</span> <span class="n">params</span><span class="p">,</span> <span class="n">cv</span><span class="o">=</span><span class="mi">6</span><span class="p">)</span>
<span class="n">grid_search</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"># 打分</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"test score:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">grid_search</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="nb">print</span><span class="p">(</span><span class="s2">"best params:"</span><span class="p">,</span> <span class="n">grid_search</span><span class="o">.</span><span class="n">best_params_</span><span class="p">)</span>
<span class="c1"># 最佳条件和上面的for循环一样。</span>
<span class="c1"># 注意: grid_search.best_score_ 中存储的是交叉验证的最高分，而不是在测试集上的得分。</span>
<span class="nb">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\n</span><span class="s2">best_score_:</span><span class="si">{:0.3f}</span><span class="s2">"</span><span class="o">.</span><span class="n">format</span><span class="p">(</span><span class="n">grid_search</span><span class="o">.</span><span class="n">best_score_</span><span class="p">))</span>
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<pre>test score:0.819
best params: {'alpha': 0.01, 'max_iter': 100}
best_score_:0.865
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<h1 id="%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%8F%AF%E4%BF%A1%E5%BA%A6%E8%AF%84%E4%BC%B0">分类模型的可信度评估<a class="anchor-link" href="#%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E7%9A%84%E5%8F%AF%E4%BF%A1%E5%BA%A6%E8%AF%84%E4%BC%B0">¶</a></h1>
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<h2 id="%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E4%B8%AD%E7%9A%84%E9%A2%84%E6%B5%8B%E5%87%86%E7%A1%AE%E7%8E%87">分类模型中的预测准确率<a class="anchor-link" href="#%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E4%B8%AD%E7%9A%84%E9%A2%84%E6%B5%8B%E5%87%86%E7%A1%AE%E7%8E%87">¶</a></h2>
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<div class="jp-InputPrompt jp-InputArea-prompt">In [1]:</div>
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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="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">sklearn.datasets</span> <span class="kn">import</span> <span class="n">make_blobs</span>
<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
<span class="n">X</span><span class="p">,</span> <span class="n">y</span><span class="o">=</span> <span class="n">make_blobs</span><span class="p">(</span><span class="n">n_samples</span><span class="o">=</span><span class="mi">200</span><span class="p">,</span> <span class="n">random_state</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">centers</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">cluster_std</span><span class="o">=</span><span class="mi">5</span><span class="p">)</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">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 假设深红色 1，浅色0</span>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 导入高斯贝叶斯模型</span>
<span class="kn">from</span> <span class="nn">sklearn.naive_bayes</span> <span class="kn">import</span> <span class="n">GaussianNB</span>
<span class="c1"># 数据集拆分</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">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">68</span><span class="p">)</span>
<span class="c1"># 训练高斯贝叶斯模型</span>
<span class="n">gnb</span><span class="o">=</span><span class="n">GaussianNB</span><span class="p">()</span>
<span class="n">gnb</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="n">predict_prob</span> <span class="o">=</span> <span class="n">gnb</span><span class="o">.</span><span class="n">predict_proba</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="nb">print</span><span class="p">(</span><span class="n">predict_prob</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="c1">#(50, 2)</span>
<span class="n">predict_prob</span><span class="p">[:</span><span class="mi">3</span><span class="p">]</span>
<span class="c1"># 第一行是归为2个分类的概率 98.8% vs 1.2%</span>
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<pre>(50, 2)
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<pre>array([[0.98849996, 0.01150004],
[0.0495985 , 0.9504015 ],
[0.01648034, 0.98351966]])</pre>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># 可视化分类过程中的表现</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</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="mf">0.5</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="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mf">0.5</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</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="mf">0.5</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="o">.</span><span class="n">max</span><span class="p">()</span><span class="o">+</span><span class="mf">0.5</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">gnb</span><span class="o">.</span><span class="n">predict_proba</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="mi">1</span><span class="p">]</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="c1">#绘制等高线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">contourf</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">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">summer</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</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="n">X_train</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_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</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_test</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</span><span class="p">)</span>
<span class="c1"># 设置坐标轴范围 </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="c1"># 设置横纵轴的单位</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</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">"GaussianNB predict_proba"</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 背景颜色代表概率值。半透明的点是测试集。</span>
<span class="c1"># 并不是每个分类算法都有 predict_proba 属性。</span>
<span class="c1"># 不过我们还可以使用另一种方法检查分类的可信度，就是决定系数 decision_function.</span>
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<h2 id="%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E4%B8%AD%E7%9A%84%E5%86%B3%E5%AE%9A%E7%B3%BB%E6%95%B0">分类模型中的决定系数<a class="anchor-link" href="#%E5%88%86%E7%B1%BB%E6%A8%A1%E5%9E%8B%E4%B8%AD%E7%9A%84%E5%86%B3%E5%AE%9A%E7%B3%BB%E6%95%B0">¶</a></h2>
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<p>决定系数只返回一个值，正数代表属于分类1，负数代表属于分类2.</p>
<p>由于高斯朴素贝叶斯没有 decision_function，我们换成 支持向量机SVC 算法来进行建模。</p>
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<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.svm</span> <span class="kn">import</span> <span class="n">SVC</span>
<span class="n">svc</span><span class="o">=</span><span class="n">SVC</span><span class="p">()</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="n">dec_func</span><span class="o">=</span> <span class="n">svc</span><span class="o">.</span><span class="n">decision_function</span><span class="p">(</span><span class="n">X_test</span><span class="p">)</span>
<span class="c1"># 打印决定系数的前5个</span>
<span class="nb">print</span><span class="p">(</span><span class="n">dec_func</span><span class="p">[:</span><span class="mi">5</span><span class="p">])</span>
<span class="c1"># 负数一类，正数一类。</span>
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<pre>[-1.36071347 1.53694862 1.78825594 -0.96133081 1.81826853]
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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"># 可视化工作原理</span>
<span class="n">Z</span><span class="o">=</span><span class="n">svc</span><span class="o">.</span><span class="n">decision_function</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="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="c1"># 绘制等高线</span>
<span class="n">plt</span><span class="o">.</span><span class="n">contourf</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">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">summer</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.8</span><span class="p">)</span>
<span class="c1"># 绘制散点图</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_train</span><span class="p">[:,</span><span class="mi">0</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="n">c</span><span class="o">=</span><span class="n">y_train</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">)</span>
<span class="n">plt</span><span class="o">.</span><span class="n">scatter</span><span class="p">(</span><span class="n">X_test</span><span class="p">[:,</span><span class="mi">0</span><span class="p">],</span> <span class="n">X_test</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_test</span><span class="p">,</span> <span class="n">cmap</span><span class="o">=</span><span class="n">plt</span><span class="o">.</span><span class="n">cm</span><span class="o">.</span><span class="n">cool</span><span class="p">,</span> <span class="n">edgecolor</span><span class="o">=</span><span class="s1">'k'</span><span class="p">,</span> <span class="n">alpha</span><span class="o">=</span><span class="mf">0.6</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">"SVC decision_function"</span><span class="p">)</span>
<span class="c1"># 设置横纵轴的单位</span>
<span class="n">plt</span><span class="o">.</span><span class="n">xticks</span><span class="p">(())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">yticks</span><span class="p">(())</span>
<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span>
<span class="c1"># 和上图类似，有一个斜对角线分界线。</span>
<span class="c1"># 但是深背景色的核心位置不同。处于区域中心的是高度可信的，而边缘区域和交界区域的，则是“模棱两可”区域。</span>
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<p>本例汇总使用的是二元分类任务，但是 predict_proba 和 decision_function 同样适用于多元分类任务。</p>
<ul>
<li>感兴趣的读者可以调整 make_blobs 的 centers 参数进行试验。</li>
</ul>
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<h2 id="%E5%85%B6%E5%AE%83%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87">其它模型评价指标<a class="anchor-link" href="#%E5%85%B6%E5%AE%83%E6%A8%A1%E5%9E%8B%E8%AF%84%E4%BB%B7%E6%8C%87%E6%A0%87">¶</a></h2>
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<p>之前一直使用的 .score(),</p>
<ul>
<li>对于分类模型，给出的是模型分类的准确率(accuracy)；</li>
<li>对于回归模型，给出的是回归分析中的R^2分数，翻译为 可决系数，或 拟合优度。（决定系数）<ul>
<li>R^2 的计算方法是用 “回归平方和”(explain sum of squares, ESS) 除以“总变差”(total sum of squares, TSS)</li>
</ul>
</li>
</ul>
<p>R^2 = 1 - 求和(y-y_hat)^2/求和(y-y_bar)^2</p>
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<p>其它指标</p>
<ul>
<li>精度 Precision</li>
<li>召回率 Recall</li>
<li>f1分数 f1-score</li>
<li>ROC(Receiver Operating Characteristic Curve)</li>
<li>AUC(Area Under Curve)</li>
</ul>
<p>这些指标经常和 网格搜索算法 配合使用，选择合适的模型和参数。</p>
<ul>
<li>如果使用 GridSearchCV 类，需要改变评分方法，只需要修改 scoring 参数即可。</li>
<li>随机森林，修改评分为 roc_auc:<ul>
<li>grid=GridSearchCV(RandomForestClassifier(), param_grid=param_grid, scoring='roc_auc')</li>
</ul>
</li>
</ul>
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