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简介
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5 years ago
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机器学习概述
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常见机器学习算法
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<a href="../kNN.html">
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近朱者赤近墨者黑-kNN
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</a>
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<a href="../linear_regression.html">
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最简单的回归算法-线性回归
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</a>
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</li>
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<li class="chapter " data-level="1.3.3" data-path="../logistic_regression.html">
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<a href="../logistic_regression.html">
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使用回归的思想进行分类-逻辑回归
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</a>
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</li>
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<li class="chapter " data-level="1.3.4" data-path="../decision_tree.html">
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<a href="../decision_tree.html">
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最接近人类思维的算法-决策树
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</a>
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</li>
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<li class="chapter " data-level="1.3.5" data-path="../random_forest.html">
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<a href="../random_forest.html">
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群众的力量是伟大的-随机森林
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</a>
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</li>
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<li class="chapter " data-level="1.3.6" data-path="../kMeans.html">
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<a href="../kMeans.html">
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物以类聚人以群分-kMeans
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</a>
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</li>
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<li class="chapter " data-level="1.3.7" data-path="../AGNES.html">
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<a href="../AGNES.html">
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以距离为尺-AGNES
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.4" data-path="../metrics.html">
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<a href="../metrics.html">
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模型评估指标
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</a>
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<ul class="articles">
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<li class="chapter " data-level="1.4.1" data-path="../classification_metrics.html">
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<a href="../classification_metrics.html">
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分类性能评估指标
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</a>
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</li>
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<li class="chapter " data-level="1.4.2" data-path="../regression_metrics.html">
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<a href="../regression_metrics.html">
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回归性能评估指标
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</a>
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</li>
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<li class="chapter " data-level="1.4.3" data-path="../cluster_metrics.html">
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<a href="../cluster_metrics.html">
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聚类性能评估指标
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</a>
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</li>
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</ul>
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</li>
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<li class="chapter " data-level="1.5" data-path="../sklearn.html">
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<a href="../sklearn.html">
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使用sklearn进行机器学习
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</a>
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</li>
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<li class="chapter " data-level="1.6" >
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<span>
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综合实战案例
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</span>
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<ul class="articles">
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<li class="chapter " data-level="1.6.1" >
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5 years ago
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<span>
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泰坦尼克生还预测
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter " data-level="1.6.1.1" data-path="introduction.html">
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5 years ago
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<a href="introduction.html">
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简介
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.2" data-path="EDA.html">
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5 years ago
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<a href="EDA.html">
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探索性数据分析(EDA)
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</a>
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</li>
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5 years ago
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<li class="chapter active" data-level="1.6.1.3" data-path="feature engerning.html">
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5 years ago
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<a href="feature engerning.html">
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特征工程
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.4" data-path="fit and predict.html">
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5 years ago
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<a href="fit and predict.html">
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构建模型进行预测
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.1.5" data-path="tuning.html">
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5 years ago
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<a href="tuning.html">
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调参
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</a>
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</li>
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</ul>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2" >
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5 years ago
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<span>
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使用强化学习玩乒乓球游戏
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</span>
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<ul class="articles">
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5 years ago
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<li class="chapter " data-level="1.6.2.1" data-path="../pingpong/what is reinforce learning.html">
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5 years ago
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<a href="../pingpong/what is reinforce learning.html">
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什么是强化学习
|
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2.2" data-path="../pingpong/Policy Gradient.html">
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5 years ago
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<a href="../pingpong/Policy Gradient.html">
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Policy Gradient原理
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</a>
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</li>
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5 years ago
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<li class="chapter " data-level="1.6.2.3" data-path="../pingpong/coding.html">
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5 years ago
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<a href="../pingpong/coding.html">
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使用Policy Gradient玩乒乓球游戏
|
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</a>
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</li>
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</li>
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5 years ago
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</ul>
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</li>
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<li class="chapter " data-level="1.7" data-path="../recommand.html">
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<a href="../recommand.html">
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实训推荐
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</a>
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5 years ago
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<!-- Title -->
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<h1>
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<i class="fa fa-circle-o-notch fa-spin"></i>
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<a href=".." >特征工程</a>
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</h1>
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<section class="normal markdown-section">
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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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|
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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>))
|
||
|
|
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>)
|
||
|
|
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>)
|
||
|
|
plt.show()
|
||
|
|
</code></pre>
|
||
|
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<p><img src="../img/54.jpg" alt=""></p>
|
||
|
|
<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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|
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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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|
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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>
|
||
|
|
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>
|
||
|
|
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>
|
||
|
|
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>
|
||
|
|
|
||
|
|
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>
|
||
|
|
<p>很明显，花费越多生还率越高，金钱决定命运。</p>
|
||
|
|
<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>
|
||
|
|
<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>
|
||
|
|
<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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|
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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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|
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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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|
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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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