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周志华《机器学习》课后习题解答系列（四）：Ch3 - 线性模型.html
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周志华《机器学习》课后习题解答系列（四）：Ch3 - 线性模型.html
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<title>周志华《机器学习》课后习题解答系列（四）：Ch3 - 线性模型</title>
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<h2>本章概要</h2>
<p>本章开始涉及编程练习，这里采用<strong>Python-sklearn</strong>的方式，环境搭建可参考<a href="http://blog.csdn.net/snoopy_yuan/article/details/61211639"> 数据挖掘入门：Python开发环境搭建（eclipse-pydev模式）</a>.</p>
<p>相关答案和源代码托管在我的Github上：<a href="https://github.com/PY131/Machine-Learning_ZhouZhihua">PY131/Machine-Learning_ZhouZhihua</a>.</p>
<p>本章讲述<strong>线性模型</strong>（linear model），相关内容包括：</p>
<ul>
<li>线性回归（linear regression）</li>
</ul>
<blockquote>
<p>序关系（order）、均方差（square error）最小化、欧式距离（Euclidean distance）、<strong>最小二乘法</strong>（least square method）、参数估计（parameter estimation）、多元线性回归（multivariate linear regression）、广义线性回归（generalized linear model）、对数线性回归（log-linear regression）；</p>
</blockquote>
<ul>
<li>对数几率回归（逻辑回归）（logistic regression）</li>
</ul>
<blockquote>
<p>分类、Sigmoid函数、对数几率（log odds / logit）、极大似然法（maximum likelihood method）；</p>
</blockquote>
<ul>
<li>线性判别分析（linear discriminant analysis - LDA）</li>
</ul>
<blockquote>
<p>类内散度（within-class scatter）、类间散度（between-class scatter）；</p>
</blockquote>
<ul>
<li>多分类学习（multi-classifier）</li>
</ul>
<blockquote>
<p>拆解法，一对一（One vs One - OvO）、一对其余（OvR）、多对多（MvM）、纠错输出码（ECOC）、编码矩阵（coding matrix）、二元码、多标记学习（multi-label learning）；</p>
</blockquote>
<ul>
<li>类别不平衡（class-imbalance）</li>
</ul>
<blockquote>
<p>再缩放（rescaling）、欠采样（undersampling）、过采样（oversampling）、阈值移动（threshold-moving）；</p>
</blockquote>
<h2>习题解答</h2>
<h4>3.1 线性回归模型偏置项</h4>
<blockquote>
<p><img src="Ch3/3.1.png" /></p>
</blockquote>
<p>偏置项b在数值上代表了自变量取0时，因变量的取值；</p>
<p>1.当讨论变量x对结果y的影响，不用考虑b；
2.可以用变量归一化（max-min或z-score）来消除偏置。</p>
<hr />
<h4>3.2 证明对数似然函数是凸函数（参数存在最优解）</h4>
<blockquote>
<p><img src="Ch3/3.2.png" /></p>
</blockquote>
<p>直接给出证明结果如下图：</p>
<blockquote>
<p><img src="Ch3/3.2.1.png" /></p>
</blockquote>
<hr />
<h4>3.3 编程实现对率回归</h4>
<blockquote>
<p><img src="Ch3/3.3.png" /></p>
</blockquote>
<p>所使用的数据集如下：</p>
<blockquote>
<p><img src="Ch3/3.3.1.png" /></p>
</blockquote>
<p>本题是本书的第一个编程练习，采用了自己编程实现和调用sklearn库函数两种不同的方式（<a href="https://github.com/PY131/Machine-Learning_ZhouZhihua/tree/master/ch3_linear_model/3.3_logistic_regression_watermelon/">查看完整代码</a>）：</p>
<p>具体的实现过程见：<a href="http://blog.csdn.net/snoopy_yuan/article/details/63684219">周志华《机器学习》课后习题解答系列（四）：Ch3.3 - 编程实现对率回归</a></p>
<hr />
<h4>3.4 比较k折交叉验证法与留一法</h4>
<blockquote>
<p><img src="Ch3/3.4.png" /></p>
</blockquote>
<p>本题采用UCI中的<a href="http://archive.ics.uci.edu/ml/datasets/Iris">iris data set</a> 和 <a href="http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center">Blood Transfusion Service Center Data Set</a> 数据集，借助sklearn实现（<a href="https://github.com/PY131/Machine-Learning_ZhouZhihua/tree/master/ch3_linear_model/3.4_cross_validation">查看完整代码</a>）。</p>
<p>具体的实现过程见：<a href="http://blog.csdn.net/snoopy_yuan/article/details/64131129">周志华《机器学习》课后习题解答系列（四）：Ch3 - 3.4.交叉验证法练习</a></p>
<hr />
<h4>3.5 编程实现线性判别分析</h4>
<blockquote>
<p><img src="Ch3/3.5.png" /></p>
</blockquote>
<p>本题采用题3.3的西瓜数据集，采用基于sklearn实现和自己独立编程实现两种方式（<a href="https://github.com/PY131/Machine-Learning_ZhouZhihua/tree/master/ch3_linear_model/3.5_LDA">查看完整代码</a>）。</p>
<p>具体的实现过程见：<a href="http://blog.csdn.net/snoopy_yuan/article/details/64443841">周志华《机器学习》课后习题解答系列（四）：Ch3 - 3.5.编程实现线性判别分析</a></p>
<hr />
<h4>3.6 线性判别分析的非线性拓展思考</h4>
<blockquote>
<p><img src="Ch3/3.6.png" /></p>
</blockquote>
<p>给出两种思路：</p>
<ul>
<li>参考书p57，采用<strong>广义线性模型</strong>，如 y-> ln(y)。</li>
<li>参考书p137，采用<strong>核方法</strong>将非线性特征空间隐式映射到线性空间，得到<strong>KLDA</strong>（核线性判别分析）。</li>
</ul>
<hr />
<h4>3.7 最优ECOC编码方式</h4>
<blockquote>
<p><img src="Ch3/3.7.png" /></p>
</blockquote>
<p>参考书p65，<em>对于同等长度的编码，理论上来说，任意两个类别间的编码距离越远，纠错能力越强</em>。那么如何实现呢，可参考文献<a href="http://www.ccs.neu.edu/home/vip/teach/MLcourse/4_boosting/lecture_notes/ecoc/ecoc.pdf">Error-Correcting Output Codes</a>。下图是截取文中的关于在较少类时采用<strong>exhaustive codes</strong>来生成最优ECOC二元码的过程：</p>
<blockquote>
<p><img src="Ch3/3.7.1.png" /></p>
</blockquote>
<p>采用文中方法，每两类的Hamming Distance均达到了码长的一半，这也是最优的编码方式之一。</p>
<hr />
<h4>3.9 多分类到二分类分解、类别不平衡</h4>
<blockquote>
<p><img src="Ch3/3.9.png" /></p>
</blockquote>
<p>参考书p66，<em>对OvR、MvM来说，由于对每类进行了相同的处理，其拆解出的二分类任务中类别不平衡的影响会相互抵销，因此通常不需专门处理。</em></p>
<p>以<strong>OvR</strong>（一对其余）为例，由于其每次以一个类为正其余为反（参考书p63），共训练出N个分类器，在这一过程中，类别不平衡由O的遍历而抵消掉。</p>
<hr />
</body>
</html>
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