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
｜
69
表5-4：线性回归系数
x
1
x
2
x
3
b
one-hot
编码
166.67 666.67
-
833.33 3333.33
虚拟编码
0 500
-
1000 3500
5.1.3
效果编码
另一种分类变量编码是
效果编码
。效果编码与虚拟编码非常相似，区别在于参照类是用全
部由
-
1
组成的向量表示的，参见表
5-5
。
表5-5：表示3个城市的分类变量的效果编码
e
1
e
2
San Francisco 1 0
New York 0 1
Seattle
-
1
-
1
效果编码与虚拟编码非常相似，但它的线性回归模型更容易解释。例
5-2
演示了使用效果
编码作为输入的情况。截距项表示目标变量的整体均值，各个系数表示了各个类别的均
值与整体均值之间的差。（这称为类别或水平的
主效果
，效果编码的名称就是由此而来。）
one-hot
编码实际上也可以得到同样的截距和系数，但它的每个城市都有一个线性系数。在
效果编码中，没有单独的特征来表示参照类，所以参照类的效果需要单独计算，它是所有
其他类别的系数的相反数之和。（参见
UCLA IDRE
网站上的“
FAQ: What is effect coding?
”
以获得更多详细信息。）
例
5-2
使用效果编码的线性回归
>>> effect_df = dummy_df.copy()
>>> effect_df.ix[3:5, ['city_SF', 'city_Seattle']] = -1.0
>>> effect_df
Rent city_SF city_Seattle
0 3999 ...
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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.