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2023/11/07/使用SVM进行垃圾短信分类/index.html

@@ -164,7 +164,8 @@ <h2 class="title">使用SVM进行垃圾短信分类</h2>
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-        <h1 id="使用SVM进行垃圾短信分类"><a href="#使用SVM进行垃圾短信分类" class="headerlink" title="使用SVM进行垃圾短信分类"></a>使用SVM进行垃圾短信分类</h1><h2 id="1-数据集"><a href="#1-数据集" class="headerlink" title="1.数据集"></a>1.数据集</h2><p>​    <a target="_blank" rel="noopener" href="https://www.kaggle.com/code/sadeghjalalian/96-accuracy-svm-spam-text-message-classification/comments">来自kaggle</a></p>
+        <p><a target="_blank" rel="noopener" href="https://kinferiority.github.io/2023/11/07/%E5%85%B3%E4%BA%8E%E4%BD%BF%E7%94%A8SVM%E8%BF%9B%E8%A1%8C%E5%9E%83%E5%9C%BE%E7%9F%AD%E4%BF%A1%E5%88%86%E7%B1%BB%E7%9A%84%E5%9B%9E%E9%A1%BE/">相互知识总结</a></p>
+<h1 id="使用SVM进行垃圾短信分类"><a href="#使用SVM进行垃圾短信分类" class="headerlink" title="使用SVM进行垃圾短信分类"></a>使用SVM进行垃圾短信分类</h1><h2 id="1-数据集"><a href="#1-数据集" class="headerlink" title="1.数据集"></a>1.数据集</h2><p>​    <a target="_blank" rel="noopener" href="https://www.kaggle.com/code/sadeghjalalian/96-accuracy-svm-spam-text-message-classification/comments">来自kaggle</a></p>
 <figure class="highlight plain"><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></pre></td><td class="code"><pre><span class="line">Category,Message</span><br><span class="line">ham,"Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat..."</span><br><span class="line">ham,Ok lar... Joking wif u oni...</span><br><span class="line">spam,Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&amp;C's apply 08452810075over18's</span><br><span class="line">ham,U dun say so early hor... U c already then say...</span><br></pre></td></tr></table></figure>
 <h3 id="1-1阅读数据集相关信息"><a href="#1-1阅读数据集相关信息" class="headerlink" title="1.1阅读数据集相关信息"></a>1.1阅读数据集相关信息</h3><figure class="highlight plain"><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></pre></td><td class="code"><pre><span class="line">data = pd.read_csv("SPAM text message 20170820 - Data.csv")</span><br><span class="line">print(data.head())</span><br><span class="line">print(data.info())</span><br><span class="line">print(data.groupby("Category").describe())</span><br><span class="line">'''</span><br><span class="line">         Message                                                               </span><br><span class="line">           count unique                                                top freq</span><br><span class="line">Category                                                                       </span><br><span class="line">ham         4825   4516                             Sorry, I'll call later   30</span><br><span class="line">spam         747    641  Please call our customer service representativ...    4</span><br><span class="line">'''</span><br></pre></td></tr></table></figure>
 <h3 id="1-2创建饼图"><a href="#1-2创建饼图" class="headerlink" title="1.2创建饼图"></a>1.2创建饼图</h3><figure class="highlight plain"><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></pre></td><td class="code"><pre><span class="line"># 创建新列length统计信息长度</span><br><span class="line">data['Length'] = data['Message'].apply(len)</span><br><span class="line">print(data.head())</span><br><span class="line"></span><br><span class="line"># 展示数据分布百分比</span><br><span class="line">explode = (0.1, 0)</span><br><span class="line"># explode用于指定是否要将某个扇形从饼图中分离出来。</span><br><span class="line"># 在这里，第一个元素是0.1，表示将"ham"类别的扇形分离出来，而第二个元素是0，表示"spam"类别的扇形不分离。</span><br><span class="line">fig1, ax1 = plt.subplots(figsize=(12, 7))</span><br><span class="line"># plt.subplots 通常与 ax 对象一起使用，ax 对象是子图的引用，你可以使用它来绘制图形元素。</span><br><span class="line">ax1.pie(data['Category'].value_counts(), explode=explode, labels=['ham', 'spam'], autopct='%1.1f%%',</span><br><span class="line">        shadow=True)</span><br><span class="line">"""</span><br><span class="line">data['Category'].value_counts()：返回Pandas Series 对象，这个 Series 对象是一维数据结构，其中索引是类别名称，数据是每个类别出现的次数。</span><br><span class="line">这部分计算了 "Category" 列中各个类别的计数，即 "ham" 和 "spam" 的数量。</span><br><span class="line">explode=explode：使用之前定义的explode变量来分离"ham"类别的扇形。</span><br><span class="line">labels=['ham', 'spam']：设置饼图中扇形的标签，分别为 "ham" 和 "spam"。</span><br><span class="line">autopct='%1.1f%%'：设置扇形上显示的百分比格式，保留一位小数。</span><br><span class="line">shadow=True：添加阴影效果，使饼图看起来有立体感</span><br><span class="line"></span><br><span class="line">"""</span><br><span class="line">ax1.axis('equal')</span><br><span class="line">plt.tight_layout()</span><br><span class="line">plt.legend()</span><br><span class="line">plt.show()</span><br></pre></td></tr></table></figure>
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