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<!-- Title -->
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<a href=".." >特征工程</a>
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<h1 id="特征工程">特征工程</h1>
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<p>什么是特征工程？其实每当我们拿到数据时，并不是所有的特征都是有用的，可能有许多冗余的特征需要删掉，或者根据 EDA 的结果，我们可以根据已有的特征来添加新的特征，这其实就是特征工程。</p>
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<p>接下来我们来尝试对一些特征进行处理。</p>
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<h2 id="年龄离散化">年龄离散化</h2>
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<p>年龄是一个连续型的数值特征，有的机器学习算法对于连续性数值特征不太友好，例如决策树、随机森林等 tree-base model。所以我们可以考虑将年龄转换成年龄段。例如将年龄小于 16 的船客置为 0 ，16 到 32 岁之间的置为 1 等。</p>
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<pre><code class="lang-python">data[<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">0</span>
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data.loc[data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">16</span>,<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">0</span>
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data.loc[(data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">16</span>)&(data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">32</span>),<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">1</span>
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data.loc[(data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">32</span>)&(data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">48</span>),<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">2</span>
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data.loc[(data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">48</span>)&(data[<span class="hljs-string">'Age'</span>]<=<span class="hljs-number">64</span>),<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">3</span>
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data.loc[data[<span class="hljs-string">'Age'</span>]><span class="hljs-number">64</span>,<span class="hljs-string">'Age_band'</span>]=<span class="hljs-number">4</span>
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</code></pre>
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<p><img src="../img/52.jpg" alt=""></p>
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<p>我们可以看一下转换成年龄段后，年龄段与生还率的关系。</p>
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<pre><code class="lang-python">sns.factorplot(<span class="hljs-string">'Age_band'</span>,<span class="hljs-string">'Survived'</span>,data=data,col=<span class="hljs-string">'Pclass'</span>)
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plt.show()
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</code></pre>
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<p><img src="../img/53.jpg" alt=""></p>
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<p>可以看出和我们之前 EDA 的结果相符，年龄越大，生还率越低。</p>
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<h2 id="家庭成员数量与是否孤身一人">家庭成员数量与是否孤身一人</h2>
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<p>由于家庭成员数量和是否孤身一人好想对于是否生还有影响，所以我们不妨添加新的特征。</p>
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<pre><code class="lang-python">data[<span class="hljs-string">'Family_Size'</span>]=<span class="hljs-number">0</span>
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data[<span class="hljs-string">'Family_Size'</span>]=data[<span class="hljs-string">'Parch'</span>]+data[<span class="hljs-string">'SibSp'</span>]
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data[<span class="hljs-string">'Alone'</span>]=<span class="hljs-number">0</span>
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data.loc[data.Family_Size==<span class="hljs-number">0</span>,<span class="hljs-string">'Alone'</span>]=<span class="hljs-number">1</span>
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</code></pre>
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<p>然后再可视化看一下</p>
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<pre><code class="lang-python">f,ax=plt.subplots(<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,figsize=(<span class="hljs-number">18</span>,<span class="hljs-number">6</span>))
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sns.factorplot(<span class="hljs-string">'Family_Size'</span>,<span class="hljs-string">'Survived'</span>,data=data,ax=ax[<span class="hljs-number">0</span>])
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ax[<span class="hljs-number">0</span>].set_title(<span class="hljs-string">'Family_Size vs Survived'</span>)
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sns.factorplot(<span class="hljs-string">'Alone'</span>,<span class="hljs-string">'Survived'</span>,data=data,ax=ax[<span class="hljs-number">1</span>])
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ax[<span class="hljs-number">1</span>].set_title(<span class="hljs-string">'Alone vs Survived'</span>)
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plt.close(<span class="hljs-number">2</span>)
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plt.close(<span class="hljs-number">3</span>)
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plt.show()
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</code></pre>
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<p><img src="../img/54.jpg" alt=""></p>
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<p>从图中可以很明显的看出，如果你是一个人，那么生还的几率比较低，而且对于人数大于 4 人的家庭来说生还率也比较低。感觉，这可能也是一个比较好的特征，可以再深入的看一下。</p>
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<pre><code class="lang-python">sns.factorplot(<span class="hljs-string">'Alone'</span>,<span class="hljs-string">'Survived'</span>,data=data,hue=<span class="hljs-string">'Sex'</span>,col=<span class="hljs-string">'Pclass'</span>)
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plt.show()
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</code></pre>
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<p><img src="../img/55.jpg" alt=""></p>
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<p>可以看出，除了三等舱的单身女性的生还率比非单身女性的生还率高外，单身并不是什么好事。</p>
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<h2 id="花费离散化">花费离散化</h2>
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<p>和年龄一样，花费也是一个连续性的数值特征，所以我们不妨将其离散化。</p>
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<pre><code class="lang-python">data[<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">0</span>
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data.loc[data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">7.91</span>,<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">0</span>
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data.loc[(data[<span class="hljs-string">'Fare'</span>]><span class="hljs-number">7.91</span>)&(data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">14.454</span>),<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">1</span>
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data.loc[(data[<span class="hljs-string">'Fare'</span>]><span class="hljs-number">14.454</span>)&(data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">31</span>),<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">2</span>
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data.loc[(data[<span class="hljs-string">'Fare'</span>]><span class="hljs-number">31</span>)&(data[<span class="hljs-string">'Fare'</span>]<=<span class="hljs-number">513</span>),<span class="hljs-string">'Fare_cat'</span>]=<span class="hljs-number">3</span>
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sns.factorplot(<span class="hljs-string">'Fare_cat'</span>,<span class="hljs-string">'Survived'</span>,data=data,hue=<span class="hljs-string">'Sex'</span>)
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plt.show()
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</code></pre>
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<p><img src="../img/56.jpg" alt=""></p>
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<p>很明显，花费越多生还率越高，金钱决定命运。</p>
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<h2 id="将字符串特征转换为数值型特征">将字符串特征转换为数值型特征</h2>
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<p>由于我们的机器学习模型不支持字符串，所以需要将一些有用的字符串类型的特征转换成数值型的特征，比如：性别，口岸，姓名前缀。</p>
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<pre><code class="lang-python">data[<span class="hljs-string">'Sex'</span>].replace([<span class="hljs-string">'male'</span>,<span class="hljs-string">'female'</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>],inplace=<span class="hljs-keyword">True</span>)
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data[<span class="hljs-string">'Embarked'</span>].replace([<span class="hljs-string">'S'</span>,<span class="hljs-string">'C'</span>,<span class="hljs-string">'Q'</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>,<span class="hljs-number">2</span>],inplace=<span class="hljs-keyword">True</span>)
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data[<span class="hljs-string">'Initial'</span>].replace([<span class="hljs-string">'Mr'</span>,<span class="hljs-string">'Mrs'</span>,<span class="hljs-string">'Miss'</span>,<span class="hljs-string">'Master'</span>,<span class="hljs-string">'Other'</span>],[<span class="hljs-number">0</span>,<span class="hljs-number">1</span>,<span class="hljs-number">2</span>,<span class="hljs-number">3</span>,<span class="hljs-number">4</span>],inplace=<span class="hljs-keyword">True</span>)
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</code></pre>
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<h2 id="删掉没多大用处的特征">删掉没多大用处的特征</h2>
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<ul>
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<li>姓名：难道姓名和生死有关系？这也太玄乎了，我不信，所以把它删掉</li>
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<li>年龄：由于已经根据年龄生成了新的特征“年龄段”，所以这个特征也需要删除。</li>
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<li>票：票这个特征感觉是一堆随机的字符串，所以删掉。</li>
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<li>花费：和年龄一样，删掉。</li>
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<li>船舱：由于有很多缺失值，不好填充，所以可以考虑删掉。</li>
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<li>船客ID：ID和生死应该没啥关系，所以删掉。</li>
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</ul>
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<pre><code class="lang-python">data.drop([<span class="hljs-string">'Name'</span>,<span class="hljs-string">'Age'</span>,<span class="hljs-string">'Ticket'</span>,<span class="hljs-string">'Fare'</span>,<span class="hljs-string">'Cabin'</span>,<span class="hljs-string">'PassengerId'</span>],axis=<span class="hljs-number">1</span>,inplace=<span class="hljs-keyword">True</span>)
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</code></pre>
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