April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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65
第 5 章
分类变量：自动化时代的数据计数
顾名思义，
分类变量
是用来表示类别或标记的。例如，分类变量可以表示世界上的主要城
市、一年中的四季、企业所属行业（石油、旅游、科技），等等。在实际的数据集中，类
别的数量总是有限的。类别可以用数字表示，但与数值型变量不同，分类变量的值是不能
被排序的。（作为行业类型，石油和旅游之间是分不出大小的。）它们又称为
无序变量
。
可以用一个简单的问题作为能否使用分类变量的试金石：“我们是需要知道两个值有
多大
不同，还是只需要知道它们
是否
不同？”
500
美元的股票价格是
100
美元的股票价格的
5
倍，所以，股票价格应该用连续型数值变量表示。另一方面，公司所属行业（石油、旅
游、科技，等等）就应该用分类变量表示。
大型分类变量在交易记录中是极其常见的。例如，很多
Web
服务使用
ID
来跟踪用户，
ID
就是一个分类变量，它的值根据服务的用户数量的不同，可能有几百个到几亿个。大型分
类变量的另一个例子是互联网交易中的
IP
地址。尽管用户
ID
和
IP
地址是用数值表示的，
但它们是分类变量，因为它们的大小与当前任务通常是没有关系的。举例来说，当进行个
人交易中的欺诈检测时，
IP
地址是个相关变量——有些
IP
地址或子网会生成更多的欺诈
交易。但地址为
164.203.x.x
的子网不会天生比
164.202.x.x
的子网更具欺诈性，子网地址
的数值在这里并不重要。
文档语料库的词汇表可以表示为一个大型分类变量，类别就是唯一的单词。表示如此多的
不同类别需要很高的计算成本。如果一个类别（如一个单词）在一个数据点 ...
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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.