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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188
｜
第
5
章
本节关键术语
欠采样
在分类模型中使用较少的普遍类记录。
同义词
下采样
过采样
在分类模型中使用较多的稀有类记录，如果有必要，可以进行
Bootstrap
抽样。
同义词
上采样
上加权和下加权
在模型的稀有（或普遍）类上添加更多（或更少）权重。
数据生成
与
Bootstrap
抽样类似，只是通过
Bootstrap
抽出的每个新记录都与它的来源有一点
儿差异。
z
分数
标准化后的结果值。
K
最近邻计算中使用的近邻数量。
5.5.1
欠采样
如果你有足够多的数据，就像贷款数据一样，就可以对普遍类进行
欠采样
（或称下采样），
从而使得用于建模的数据在
0
和
1
之间更加平衡
。下采样的基本思想是多数类的数据中有
很多冗余记录。使用一个规模更小的、更加平衡的数据集可以令模型性能更佳，数据准
备、模型探索和试验也变得更加容易。
那么，多少数据才算“足够”呢？这取决于具体的应用，但一般来说，非普遍类中有几万
条记录就足够了。
1
和
0
之间的区分越容易，所需的数据就越少。
5.3
节中分析了贷款数据
，这个分析基于一个平衡的训练集：一半的贷款被还清，另一半
贷款则发生违约。预测值也一样：一半的概率小于
0.5
，另一半大于
0.5
。在完整的数据集
中，只有
19%
的贷款发生违约，如以下
R
代码所示：
mean(full_train_set$outcome=='default')
[1] 0.1889455
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