January 2018
Intermediate to advanced
301 pages
8h 54m
Chinese
Content preview from Python机器学习基础教程
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280
｜
第
8
章
虽然技术债问题在大规模的长期项目中特别紧迫，但即使是短期和较小的系统，吸取的教
训也有助于我们构建更好的软件。
8.3
测试生产系统
在这本书中，我们介绍了如何基于事先收集的测试集来评估算法的预测结果。这被称为
离
线评估
（
offline evaluation
）。但如果你的机器学习系统是面向用户的，那么这只是评估算
法的第一步。下一步通常是
在线测试
（
online testing
）或
实时测试
（
live testing
），对在整个
系统中使用算法的结果进行评估。改变网站向用户呈现的推荐结果或搜索结果，可能会极
大地改变用户行为，并导致意想不到的结果。为了防止出现这种意外，大部分面向用户的
服务都会采用
A/B
测试
（
A/B testing
），这是一种盲的（
blind
）用户研究形式。在
A/B
测
试中，在用户不知情的情况下，为选中的一部分用户提供使用算法
A
的网站或服务，而为
其余用户提供算法
B
。对于两组用户，在一段时间内记录相关的成功指标。然后对算法
A
和算法
B
的指标进行对比，并根据这些指标在两种方法中做出选择。使用
A/B
测试让我
们能够在实际情况下评估算法，这可能有助于我们发现用户与模型交互时的意外后果。通
常情况下，
A
是一个新模型，而
B
是已建立的系统。在线测试中还有比
A/B
测试更为复杂
的机制，比如
bandit
算法
。
John Myles White
的
Bandit Algorithms for Website Optimization
（
O
’
Reilly
出版社，
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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.