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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4.8
将特征离散化
｜
75
Price Bathrooms Square_Feet Outlier Log_Of_Sqaure_Feet
0 534433 2.0 1500 0 7.313220
1 392333 3.5 2500 0 7.824046
2 293222 2.0 1500 0 7.313220
3 4322032 116.0 48000 1 10.778956
讨论
和识别异常值一样，处理异常值时也不存在一个绝对准则。应该基于两个方面来考虑对
异常值的处理。第一，要弄清楚是什么让它们成为异常值的。如果你认为它们是错误的
观察值，比如它们来自一个坏掉的传感器或者是被记错了的值，那么就要丢弃它们或者
用
NaN
来替换异常值，因为我们无法信任这些值。但是，如果你认为这些异常值真的就
是极端值（例如一幢大宅子有
200
间卧室），那么把它们标记为异常值或者对它们的值
进行转换，是更合理的做法。
第二，应该基于机器学习的目标来处理异常值。例如，如果想要基于房屋的特征来预测
其价格，那么可以合理地假设有
100
间卧室的大宅子的价格是由不同于普通家庭住宅的
特征驱动的。此外，如果使用一个在线住房贷款的
Web
应用的部分数据来训练一个模型，
那么就要假设潜在用户中不存在想要买一栋有几百间卧室的豪宅的亿万富翁。
所以，对于异常值到底要如何处理呢？首先，想一想它们为什么是异常值，然后对于数
据要有一个最终的目标。最重要的是，要记住“决定不处理异常值”本身就是一个有潜
在影响的决定。
另外，如果数据中有异常值，那么采用标准化方法做缩放就不太合适了，因为平均值和 ...
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