April 2019
Intermediate to advanced
172 pages
4h 39m
Chinese
Content preview from 精通特征工程
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简单而又奇妙的数值
｜
9
2.2.2
区间量化
（
分箱
）
在这个练习中，我们使用来自于
Yelp
点评网站数据竞赛（
http://www.yelp.com/dataset_
challenge
）第
6
轮的数据，并用这些数据创建一个规模小得多的分类数据集。
Yelp
数据集
是用户对商家的点评数据，这些商家来自于北美和欧洲的
10
个城市，每个商家都用
0
个
或多个分类进行标记。
Yelp
点评数据集
（
第
6
轮
）
的统计信息
• 782
个商业分类。
•
整个数据集包含
1 569 264
（
≈ 160
万）条点评和
61 184
个商家。
•
就点评数量而言，“
Restaurants
”（
990 627
条点评）和“
Nightlife
”（
210 028
条点评）
是最普遍的分类。
•
没有商家既属于餐馆又属于夜生活场所，所以这两个点评分组中没有重叠。
每个商家都有一个点评数量。假设我们的任务是使用协同过滤方法预测某用户给某商家的打
分。点评数量会是一个非常有用的输入特征，因为人气和高评分之间通常有很强的相关性。
现在的问题就是，我们应该使用原始点评数量，还是应该对其做进一步的处理？图
2-4
是根
据例
2-2
生成的，它给出了所有商家点评数量的直方图。从中可以看出，它和前一个例子中
的收听次数具有相同的模式：大多数商家的点评数量很少，但有些商家具有几千条点评。
例
2-2
Yelp
数据集中的商家点评数量可视化
>>> import pandas as pd
>>> import json
# 加载商家数据
>>> biz_file = open('yelp_academic_dataset_business.json') ...
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I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.