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Copy path周志华《机器学习》课后习题解答系列（七）：Ch6.2 - 支持向量分析实验.html
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周志华《机器学习》课后习题解答系列（七）：Ch6.2 - 支持向量分析实验.html
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<title>周志华《机器学习》课后习题解答系列（七）：Ch6.2 - 支持向量分析实验</title>
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<p>查看相关答案和源代码，欢迎访问我的Github：<a href="https://github.com/PY131/Machine-Learning_ZhouZhihua">PY131/Machine-Learning_ZhouZhihua</a>.</p>
<h2>6.2 支持向量分析实验</h2>
<blockquote>
<p><img src="Ch6/6.2.png" /></p>
<p><img src="Ch6/6.2.data.png" /></p>
</blockquote>
<p>(注：本题实验基于python，另外，sklearn库已集成了libsvm库，并在其基础上扩展形成了自带svm工具库，这里我们采用该sklearn-svm工具库开展实验）</p>
<p><a href="https://github.com/PY131/Machine-Learning_ZhouZhihua/blob/master/ch6_support_vector_machine/6.2_SVM_test/sv_compare.py">查看本实验完整代码</a></p>
<h3>数据预处理</h3>
<p>生成数据<code>watermelon_3a.csv</code>，将类别编码为 0（否），1（是），基于pandas读取数据，做出可视化界面如下：</p>
<p><img src="Ch6/6.2.scatter.png" /></p>
<h3>训练与分析</h3>
<p>采用<code>sklearn.svm.svc</code>训练并得出支持向量，实验段程序示意如下：</p>
<pre><code>```python
from sklearn import svm
# initial
svc = svm.SVC(C=1000, kernel=kernel) # classifier 1 based on linear kernel
# train
svc.fit(X, y)
# get support vectors
sv = svc.support_vectors_
```
</code></pre>
<p>绘制出决策边界，同时标记出支持向量如下图：</p>
<ol>
<li>线性核函数：</li>
</ol>
<p><img src="Ch6/6.2.linear.png" /></p>
<ol>
<li>高斯核函数：</li>
</ol>
<p><img src="Ch6/6.2.rbf.png" /></p>
<p>可以估计出，面向该题数据集，高斯核函数的拟合更好（间隔更小），且用到的支持向量更少（当前参数设置下有9个支持向量）。</p>
<h3>参考</h3>
<p>本文涉及的一些参考资料如下：</p>
<ul>
<li>sklearn官网 - <a href="http://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html">sklearn.svm.SVC</a>.</li>
<li>sklearn官网 - <a href="http://scikit-learn.org/stable/auto_examples/exercises/plot_iris_exercise.html#sphx-glr-auto-examples-exercises-plot-iris-exercise-py">SVM Exercise（使用样例）</a></li>
</ul>
</body>
</html>
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