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精通特征工程
book

精通特征工程

by Alice Zheng, Amanda Casari
April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Posts & Telecom Press
Content preview from 精通特征工程
简单而又奇妙的数值
7
特征空间中的数据
数据空间中的特征
2-2:特征空间与数据空间
2.2
 处理计数
在大数据时代,计数数据可以无限度地快速增长。用户可以无数次地重复播放一首歌曲或
一部电影,也可以使用脚本反复地检查一个热门演出是否有余票,这都可以使播放计数和
网站访问计数快速增长。当数据被大量且快速地生成时,很有可能包含一些极端值。这时
就应该检查数据的尺度,确定是应该保留数据原始的数值形式,还是应该将它们转换成二
值数据,或者进行粗粒度的分箱操作。为了说明这种思想,我们来看几个例子。
2.2.1
 二值化
Echo Nest Taste Profile
是百万歌曲数据集(
Million Song Dataset
)的正式用户数据子集,其
中包含了
100
万用户在
Echo Nest
网站上完整的音乐收听历史。下面是这个数据集的一些
重要统计信息。
Echo Nest Taste Profile
数据集的统计信息
数据集中有超过
4800
万个由用户
ID
、歌曲
ID
和收听次数构成的三元组。
完整的数据集中包括
1 019 318
个独立用户和
384 546
首独立歌曲。
假设我们的任务是创建一个向用户推荐歌曲的推荐器,它的一个功能是预测某个用户喜欢
某首歌曲的程度。既然数据中包含了实际的收听次数,那么可以用它作为预测的目标变量
吗?如果高收听次数意味着用户真的喜欢这首歌,低收听次数意味着用户对这首歌不感兴
8
2
趣,那么就可以用它作为目标变量。但是,数据表明,尽管
99%
的收听次数是
24
或更低,
还是有一些收听次数达到了几千 ...
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Publisher Resources

ISBN: 9787115509680