September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
Content preview from 基于AWS的数据科学实践
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195
第 6 章
为模型训练准备数据集
在第
5
章，我们使用
SageMaker Studio
和不同的
Python
可视化库探索了数据集。我
们使用
Amazon
客户评价数据集得到了关于产品目录的一些关键的商业洞见。此外，
我们使用
SageMaker
处理任务，
Apache Spark
和
AWS Deequ
开源库在数据集上分
析了汇总统计并运行了质量检查。
在本章，我们讨论如何通过称为“特征工程”的过程将人类可读的文本转换为机器
可读的向量。具体来说，我们会将
Amazon
客户评价数据集中的原始
review_body
列转化为
BERT
向量。我们使用这些
BERT
向量在第
7
章训练评价分类模型，并在
第
8
章优化该模型。我们也会在第
7
章更深入了解自然语言处理和
BERT
的起源。
我们会使用评价分类模型来预测来自社交网络、合作伙伴网站等的产品评价给出的
star_rating
。通过粗略地预测评价的
star_rating
，产品管理和客户服务团队可以
使用这些预测在质量问题被公开讨论时修正它们，而不是等到直接的邮件或电话再
进行修复。这将检测质量问题的平均时间从数天乃至数月，减少到数分钟或数小时。
6.1
特征选择和特征工程
人工智能和机器学习算法是操作数值和向量的数值优化方法，而非原始文本和图像。
这些向量通常被称作“嵌入”，它们被投影到高维向量空间。
算法在这一高维向量
空间运行优化。
196
｜
第
6
章
独热编码（
one-hot encoding
）是为在表数据集内编码类别数据的一种嵌入形式。通
过独热编码，我们将每个类型值表示为一个由
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