April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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78
｜
第
5
章
函数的输出范围
m
都远远小于类别数量
k
。在计算统计量时，需要使用所有散列函数进行
计算，并返回结果中最小的那个统计量。与使用单散列函数相比，使用多个散列函数可以
降低碰撞概率。这种方法的有效之处在于，散列函数的数量乘以散列表大小
m
之后，不但
可以小于类别数量
k
，而且能保持非常低的碰撞概率。
图
5-4
演示了这个过程。对于每个项目
i
，都把它映射到计数数组每一行中的某个单元。当
项目
i
t
的计数
c
t
更新时，就使用函数
h
1
…
h
d
进行散列，添加到每个单元中。
h
1
h
d
c
t
i
t
c
t
c
t
c
t
图 5-4：最小计数图
2.
防止数据泄露
因为分箱计数要依赖历史数据生成必需的统计量，所以它需要等待一段时间以完成数据收
集，这就会在学习流程中导致一点轻微的延迟。还有，当数据分布改变时，需要更新计
数。数据变化得越快，计数重新计算的频率就越高。在像定向广告这样的应用中，用户偏
好和常用查询变化得非常快，所以这个问题变得特别重要，不能适应当前数据分布的变化
意味着广告平台的巨大损失。
有人或许会问：为什么不使用同样的数据集来计算相关统计量和训练模型？这种想法太天
真了。这里最大的问题是，统计量中包含目标变量，而它正是模型试图去预测的。使用输
出去计算输入特征会导致一个非常严重的问题，那就是
数据泄露
。简单地说，数据泄露会
使模型中包含一些不应该有的信息，这些信息可以使模型获得某种不现实的优势，从而做
出更加精确的预测。出现数据泄露有多种原因，比如测试数据泄露到训练数据中，或者未
来数据泄露到过去数据中。只要模型获得了在生产环境中实时预测时不应该接触到的信 ...
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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.