April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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分类变量：自动化时代的数据计数
｜
71
就在一个贝叶斯
probit
回归模型（可用简单的更新在线训练）中使用了这种二值特征。与
此同时，其他一些研究小组则致力于特征压缩方法。
Yahoo!
的研究者们对特征散列化推
崇备至（
Weinberger
等，
2009
），但是
McMahan
等人在
Google
广告引擎上试验了特征散
列化，却没有取得什么显著的进展。而微软的其他研究者又在实践分箱计数这种思想了
（
Bilenko, 2015
）。
正如我们将看到的，所有方法都有各自的优点和缺点。我们先介绍方法本身，然后再讨论
它们的利弊得失。
5.2.1
特征散列化
散列函数
是一种确定性函数，它可以将一个可能无界的整数映射到一个有限的整数范围
[1,
m
]
中。因为输入域可能大于输出范围，所以可能有多个值被映射为同样的输出，这称
为
碰撞
。
均匀散列函数
可以确保将大致相同数量的数值映射到
m
个分箱中。
我们可以形象地将散列函数想象为一台机器，它吸入一些带数字标号的圆球（键），再把
它们分发到
m
个分箱中。标有同样数字的球总是被分发到同一个分箱中（见图
5-1
）。散列
函数在保持特征空间的同时，又可以在机器学习的训练和评价周期中减少存储空间和处理
时间。
00
01
mama
键 散列函数
（是数学，不是魔术）
散列值
dada
bottle
flags
banana
doggy
bubble
02
03
04
05
...
25
26
图 5-1：散列函数可以将键映射到分箱
我们可以为任何能表示为数值的对象（也就是任何能存储在计算机上的数据）构造散列函
数，这些对象包括数值、字符串、复杂结构，等等。 ...
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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.