October 2021
Intermediate to advanced
289 pages
8h 31m
Chinese
Content preview from 数据科学中的实用统计学(第2版)
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分类
｜
173
逻辑回归是迄今为止最常见的
GLM
形式，不过数据科学家也会遇到其他类型的
GLM
。有
时候，会使用对数链接函数代替
logit
。实际上
，对大多数应用来说，使用对数链接函数
不太可能得到特别不同的结果。在对计数数据（如一定时间内某个用户访问某个网页的次
数）建模时，经常会使用泊松分布。还有一些分布族（包括负二项分布和伽马分布）常用
于对经过的时间（如失效时间）进行建模。与逻辑回归不同，使用这些模型的
GLM
的应
用更加复杂，需要更加谨慎。除非你对这些模型方法非常熟悉，清楚地了解它们的效用和
不足，否则应尽量避免使用。
5.3.4
逻辑回归的预测值
逻辑回归的预测值是以对数发生比的形式表示的：
ˆ
Y
=
ln(Odds(
Y
=
1))
。通过逻辑响应函
数可以得到预测概率：
ˆ
1
ˆ
1e
Y
p
−
=
+
举个例子，看一下
R
中
logistic_model
模型的预测：
pred <- predict(logistic_model)
summary(pred)
Min. 1st Qu. Median Mean 3rd Qu. Max.
-2.704774 -0.518825 -0.008539 0.002564 0.505061 3.509606
在
Python
中，可以将概率转换为数据框，并使用
describe
方法得到这些分布特性：
pred = pd.DataFrame(logit_reg.predict_log_proba(X),
columns=loan_data[outcome].cat.categories) ...
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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.