April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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52
第 4 章
特征缩放的效果：从词袋到
tf-idf
词袋表示法简单易行，但远非完美。如果我们无差别地对所有单词计数，那么有些单词会
被过分强调，这是根本不必要的。回想一下第
3
章中
Emma
和乌鸦的例子，我们希望有一
种能够强调两个主要角色的文档表示方法。单词“
Emma
”和“
raven
”都出现了
3
次，但
“
the
”居然出现了
8
次 ，“
and
”也出现了
5
次 ，“
it
”和“
was
”都出现了
4
次。仅通过简单
的词频计数，无法突显出主要角色，这是这种方法的问题所在。
能挑选出像“
magnificently
”“
gleamed
”“
intimidated
”“
tentatively
”和“
reigned
”这样的单
词也是很好的，因为它们有助于确定该段文字的整体基调。它们能体现出情感，这对数据
科学家来说是非常宝贵的信息。所以，理想情况下，我们需要那种能强调出
有意义
的单词
的表示方法。
4.1
tf-idf
：
词袋的一种简单扩展
tf-idf
是在词袋方法基础上的一种简单扩展，它表示
词频
-
逆文档频率
。
tf-idf
计算的不是
数据集中每个单词在每个文档中的原本计数，而是一个归一化的计数，其中每个单词的计
数要除以这个单词出现在其中的文档数量。即：
bow(
w
,
d
) =
单词
w
在文档
d
中出现的次数
tf-idf(
w
,
d
) = bow(
w
,
d
) *
N
/ (
单词
w
出现在其中的文档数量
)
N
是数据集中的文档总数。分数
N
/ (
单词
w
出现在其中的文档的数量
)
就是所谓的
逆文档
频率
。如果一个单词出现在很多文档中，那么它的逆文档频率就接近于
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