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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无监督学习
｜
239
应该使用多少个主成分
？
如果你的目标是降低数据维度，就必须确定使用多少个主成分。最常用的方
法是使用一种特定规则来选择能解释“最多”方差的主成分。可以通过碎石
图这种可视化方式来完成这个任务，就像图
7-2
那
样。或者，也可以选择前
几个主成分，使得它们累计解释的方差超过一个阈值，比如
80%
。此外
，你
还可以检查一下载荷，确定是否可以对主成分进行直观的解释。交叉验证提
供了一种更加正式的方法来选择显著主成分的数量（参见
4.2.3
节
）。
7.1.4
对应分析
PCA
不能用于分类型数据，不过，有一种与
PCA
略有关系的方法，称为
对应分析
（
correspondence
analysis
），这种方法的目标是识别出类别之间或类别特征之间的关联。对
应分析与主成分分析的相同之处主要在于底层，即用于维度缩放的矩阵代数。对应分析主
要用于低维分类数据的图形化分析，它与
PCA
的用法不同
，
PCA
主要用于数据降维，是
处理大数据的一种预备步骤。
对应分析的输入可以看作一个表格，其中行表示一个变量，列表示另一个变量，单元格中
是记录的数量。它的输出（经过一些矩阵代数运算）是一个
双标图
（
biplot
）
，这是一种坐
标轴上带有刻度的散点图（还有这个维度所解释的方差的百分比）。坐标轴上的单位与初
始数据之间没有直接的联系。这种散点图的价值主要在于以图形化的方式说明变量之间是
如何关联的（通过图中的相邻关系）。来看一个例子，图
7-4
中给出了一些家务事的排列规
则。在纵轴上，按家务事是需要联合完成还是可以独立完成来排列；在横轴上，则按是由 ...
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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.