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周志华《机器学习》课后习题解答系列（四）：Ch3.4 - 交叉验证法练习.html
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<p>本系列主要采用<strong>Python-sklearn</strong>实现，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>
<h3>3.4 比较k折交叉验证法与留一法</h3>
<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>，基于sklearn完成练习（<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>关于数据集的介绍：</p>
<p><a href="http://baike.baidu.com/item/IRIS/4061453#viewPageContent">IRIS数据集简介 - 百度百科</a>；通过花朵的性状数据（花萼大小、花瓣大小...）来推测花卉的类别。变量属性X=4种，类别标签y公有3种，这里我们选取其中两类数据来拟合对率回归(逻辑斯蒂回归)。</p>
<p><a href="http://archive.ics.uci.edu/ml/datasets/Blood+Transfusion+Service+Center">Blood Transfusion Service Center Data Set - UCI</a>;通过献血行为（上次献血时间、总献血cc量...）的历史数据，来推测某人是否会在某一时段献血。变量属性X=4种，类别y={0,1}。该数据集相对iris要大一些。</p>
<p>具体过程如下：</p>
<h4>1. 数据导入、可视化、预分析：</h4>
<p>iris数据集十分常用，sklearn的数据包已包含该数据集，我们可以直接载入。对于transfusion数据集，我们从UCI官网上下载导入即可。</p>
<p>采用<strong>seaborn</strong>库可以实现基于matplotlib的非常漂亮的可视化呈现效果，下图是采用seaborn.pairplot()绘制的iris数据集各变量关系组合图，从图中可以看出，类别区分十分明显，分类器应该比较容易实现：</p>
<blockquote>
<p><img src="Ch3/3.4.1.png" /></p>
</blockquote>
<p>相关样例代码：</p>
<pre><code>import numpy as np
import seaborn as sns
sns.set(style="white", color_codes=True)
iris = sns.load_dataset("iris")
iris.plot(kind="scatter", x="sepal_length", y="sepal_width")
sns.pairplot(iris,hue='species')
sns.plt.show()
</code></pre>
<h4>2. 基于sklearn进行拟合与交叉验证：</h4>
<p>这里我们选择iris中的两类数据对应的样本进行分析。k-折交叉验证（1<k<n-1)可直接根据sklearn.model_selection.cross_val_predict()得到精度、F1值等度量。留一法稍微复杂一点，这里采用loop实现。</p>
<p>面向iris数据集的样例代码：</p>
<pre><code>'''
2-nd logistic regression using sklearn
'''
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn.model_selection import cross_val_predict
# log-regression lib model
log_model = LogisticRegression()
m = np.shape(X)[0]
# 10-folds CV
y_pred = cross_val_predict(log_model, X, y, cv=10)
print(metrics.accuracy_score(y, y_pred))
# LOOCV
from sklearn.model_selection import LeaveOneOut
loo = LeaveOneOut()
accuracy = 0;
for train, test in loo.split(X):
log_model.fit(X[train], y[train]) # fitting
y_p = log_model.predict(X[test])
if y_p == y[test] : accuracy += 1
print(accuracy / np.shape(X)[0])
</code></pre>
<p>得出了精度（预测准确度）结果如下：</p>
<pre><code>0.97
0.96
</code></pre>
<p>可以看到，两种方法的模型精度都十分高，这也得益于iris数据集类间散度较大。</p>
<p>同样的方法对blood-transfusion数据集得出的精度结果：</p>
<pre><code>0.76
0.77
</code></pre>
<p>也可以看到，两种交叉验证的结果相近，但是由于此数据集的类分性不如iris明显，所得结果也要差一些。同时由程序运行可以看出，<strong>LOOCV的运行时间相对较长</strong>，这一点随着数据量的增大而愈发明显。</p>
<p>所以，一般情况下选择K-折交叉验证即可满足精度要求，同时运算量相对小。</p>
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