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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159
第 5 章
分类
数据科学家经常需要将业务问题的决策自动化。一封邮件是否在试图进行钓鱼攻击？某个
客户是否有可能流失？网站用户有可能点击这个广告吗？这些都是
分类
问题。分类是一种
监督学习
，我们先使用已知结果的数据训练一个模型，然后将这个模型应用到结果未知的
数据上。分类可能是最重要的预测形式，它的目标是预测一条记录是“
1
”还是“
0
”
（钓鱼
邮件
/
非钓鱼邮件
、会点击
/
不会点击、会流失
/
不会流失），或者在某些情况下，是几个
类别中的哪一类（例如，
Gmail
将
收件箱中的邮件分为“
primary
”“
social
”“
promotional
”
和“
forums
”类）
。
通常，我们需要的不仅是简单的二元分类，我们想知道的是一个个案属于某个类别的预测
概率。大多数算法不是简单地通过模型给出一个二元分类结果，而是返回每条记录属于相
应类别的概率分数（或者倾向分数）。实际上，使用逻辑回归时，
R
的默认输出是一个对
数发生比，必须转换为倾向分数。在
Python
的
scikit-learn
中，逻辑回归与其他多数分
类方法一样，提供了两种预测方法：
predict
（返回类别）和
predict_proba
（返回属于每
种类别的概率）。可以使用滑动临界值将倾向分数转换为某种决策。常用的方法如下所示。
1.
为感兴趣的类别确定一个临界概率，如果超过这个概率，就认为记录属于这个类别。
2.
（使用任意模型）估计出一条记录属于感兴趣的类别的概率。
3.
如果这个概率超过了临界概率，就向感兴趣的类别中加入这条新记录。
临界概率越高，预测为
1
的记录就越少 ...
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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.