August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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226
｜
第
7
章
ELI5
仍在开发中
目前
ELI5
仍在积极的开发中，
scikit-learn
新版本的使用可能会遇到问题。
在本章中，我们使用了
ELI5 0.10.1
版。
ELI5
是一个非常易于使用的软件库，可用于理解和可视化分类器的决策逻辑，但是
它也无法避免底层
LIME
算法的缺点，例如只能通过样本进行解释。为了使黑盒分
类更加透明，我们需要窥探模型内部使用的“规则”。华盛顿大学团队为此创建了
一个名叫
Anchor
的项目。
7.6
案例：使用
Anchor
解释分类结果
与
LIME
同样，
Anchor
（
https://oreil.ly/qSDMl
）也不依赖模型，而且可
用于解释任何黑盒模型。作为一个解释工具，
Anchor
创建了很多规则，
也就是“
anchor
”，这些
anchor
可以解释模型的行为。阅读这些规则，不仅能够解
释模型的预测，而且还可以按照模型学习到的方式来进行预测。
与
LIME
相比，
Anchor
具有非常大的优势，它可以更好地通过规则解释模型。但是，
该软件本身非常新，而且仍在开发中。并非所有示例都适合我们，因此我们选择了
一些有助于解释分类模型的方法。
7.6.1
使用遮盖后的单词分布
Anchor
有多种使用方法。首先我们从“
unknown
”（未知）分布开始。
Anchor
可以
通过将对预测没有太大影响的单词替换成“
unknown
”的方式，对模型做出的决策
进行解释。
下面，我们继续以
ID
为
21
的文档为例进行说明。在这个示例中，分类器遇到的困
难在于，它需要从两个类别之间进行选择，但这两个类别的概率非常接近。这是一 ...
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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.