July 2019
Intermediate to advanced
365 pages
8h 13m
Chinese
Content preview from Python机器学习手册：从数据预处理到深度学习
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173
第 10 章
使用特征选择进行降维
10.0
简介
第
9
章讨论了如何在尽可能不丢失特征原始信息的情况下，通过创建新特征来降低特征矩
阵的维度，这种方法被称为特征提取（
feature extraction
）。本章将介绍另一种降维方法 ：
特征选择（
feature selection
）。特征选择会保留信息量较高的特征而丢弃信息量较低的特征。
特征选择的方法可分为三类 ：过滤器、包装器和嵌入式方法。过滤器方法根据特征的
统计信息来选择最优特征。包装器方法通过不断试错，找出一个可以产生高质量预测
值的模型的特征子集。嵌入式方法则将选择最优特征子集作为机器学习算法训练过程
的一部分。
按理来说，我们应该在本章介绍这三种方法，但是嵌入式方法与特定的机器学习算法密
切相关，在深入研究这些算法之前很难把嵌入式方法解释清楚。因此，本章只讲解过滤
器和包装器这两种特征选择方法，而把对嵌入式方法的介绍放到深入讨论机器学习算法
的章节。
10.1
数值型特征方差的阈值化
问题描述
从一组数值型特征中移除方差较小（即可能包含的信息较少）的特征。
解决方案
挑出方差大于给定阈值的特征 ：
174
｜
第
10
章
使用特征选择进行降维
#
加载库
from sklearn import datasets
from sklearn.feature_selection ...
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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.
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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.