October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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182
｜
第
5
章
这里，行表示实际响应变量，列表示预测响应变量，但反过来也很常见，在
R
中广泛使用的
caret
包就是这样的。
5.4.2
稀有类问题
在很多情况下，要预测的类别是不平衡的，某个类别会比另一个类别普遍得多，例如，正
当的保险索赔与欺诈性的保险索赔、网站浏览者与购买者。我们通常对稀有类（如欺诈性
索赔）更感兴趣，一般将其表示为
1
，而将更普遍的类表示为
0
。在典型的情形中
，
1
一般
更重要，如果将
1
错分类为
0
，那么造成的损失会比将
0
错分类为
1
更大
。例如，正确地
识别出欺诈性的保险索赔可以挽回大量金钱损失，但反过来说，如果正确地识别出非欺诈
索赔，那么节省的不过是仔细查看一下索赔文件的成本而已（如果索赔被标记为“欺诈”，
那么你就会这么做）。
在这种情况下，除非类别很容易分离，否则最准确的分类模型就是简单地将所有记录都分
类为
0
的模型
。例如，如果一个网上商店只有
0.1%
的浏览者最终会购买商品，那么一个
预测每个浏览者都会不购物就离开的模型的准确率就是
99.9%
，但
这样的模型没有什么用。
相反，我们更喜欢那种总体准确率低一点但易于找出购买者的模型，即使它将某些非购买
者错误分类。
5.4.3
精确度
、
召回率和特异度
除了准确率之外，在评估分类模型时，还经常会使用其他几个更加微妙的指标。其中几个
指标在统计学中历史悠久，特别是在生物统计学中，会使用它们来描述诊断检验的预期效
果。
精确度
测量的是预测为正例结果的准确率（见图
5-5
）。
∑
=
∑+∑
ኟ૩
ඓ܈
ኟ૩ ्ኟ૩
召回率
也称为
灵敏度
，它测量的是模型预测正例结果的能力，即模型正确识别出 ...
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.