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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统计机器学习
｜
221
•
随机森林是应用在决策树上的一种特殊的装袋法。除了对数据进行重抽样，随机森
林算法还在树分割时对预测变量进行抽样。
•
随机森林的一种有用结果是测量变量的重要性，它可以按照对模型准确度的贡献对
预测变量进行排序。
•
随机森林有一组超参数。应该使用交叉验证对这些超参数进行调优，以避免过拟合。
6.4
提升方法
集成模型已经成为了预测建模的标准方法。
提升
（
boosting
）方法是创建集成模型的一种
通用技术，它与
装袋法
（
bagging
，参
见
6.3
节）基本上是在同一时间发展起来的。与装袋
法类似，提升方法也最常用于决策树。尽管有这些相同之处，提升方法与装袋仍有很大差
别，它有很多其他用途。因此，装袋法只需要做很少的调优工作，而提升方法在应用时则
要花更多工夫。如果用汽车来比喻这两种方法的话，装袋法就是本田雅阁（可靠而稳定），
而提升方法则是保时捷（功能更强大，但需要更多的维护）。
在线性回归模型中，经常需要检查残差以确定是否可以改善拟合（参见
4.6.4
节）
。提升
方法对这种思想进行了进一步的深化，它拟合了一系列模型，每个模型都以最小化前一
个模型的误差为目标。这种算法常用的几种变体是
Adaboost
、
梯度提升
和
随机梯度提
升
，其中最常用的是随机梯度提升。实际上，如果参数选择正确，这种算法的效果可以
媲美随机森林。
本节关键术语
集成
使用一个模型集合来形成最终预测。
同义词
模型平均
提升
一种通用技术，它在多个连续的轮次中拟合一系列模型，对有较大残差的记录赋予
更大权重。
Adaboost
提升方法的一种早期版本，基于残差为数据进行重新加权。 ...
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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.