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
｜
165
判别函数
应用于预测变量，使类别之间的区别最大化的函数。
判别权重
使用判别函数得到的一种分数，用来估计记录属于某种类别的概率。
尽管判别分析中包含了多种技术，但最常使用的还是
线性判别分析
（
linear discriminant
analysis
，
LDA
）
。实际上，
R. A. Fisher
最初提出的方法与
LDA
稍微有些差别，但基本机
制是一样的。随着更高级技术（比如树模型和逻辑回归）的出现，
LDA
现
在的使用已经不
是那么广泛了。
不过，你还是会在一些应用中遇到
LDA
，
它与其他使用得更加广泛的方法（如主成分分
析，参见
7.1
节）也有联系。
不可将线性判别分析与隐狄利克雷分配（
Latent Dirichlet Allocation
，也简称
LDA
）相
混淆。隐狄利克雷分配用于文本和自然语言处理，与线性判别分析
没有关系。
5.2.1
协方差矩阵
要理解判别分析，我们必须先介绍一下两个或多个变量之间的
协方差
这个概念。协方差测
量的是两个变量
x
和
z
之间的关系。
x
和
z
表示每个变量的均值（参见
1.3.1
节 ）。
x
和
z
之间的协方差
s
x, z
如下所示：
1
,
( )( )
1
n
ii
i
xz
x xz z
s
n
=
−−
=
−
∑
其中
n
是记录的数量（注意分母是
n
–1
，不是
n
，参见
1.4.1
节
）。
与相关系数（参见
1.7
节）一样
，正的协方差表示变量之间存在正向关系，负的协方差表
示变量之间存在负向关系。不过，相关系数被限制在
–1
和
1
之间
，而协方差的值是由
x
和
z
的值决定的。在
x
和
z
的
协方差矩阵
Σ
中，对角线（行和列都是同一个变量）上是每 ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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.