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基于AWS的数据科学实践
book

基于AWS的数据科学实践

by Chris Fregly, Antje Barth
September 2022
Intermediate to advanced
549 pages
11h 27m
Chinese
China Electric Power Press Ltd.
Content preview from 基于AWS的数据科学实践
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189
概念偏移的另一个例子与本书一位早期评论者有关,其使用“
beef
”一词描述本书
的第
9
章。该名词最初被本书位于美国和德国的作者解读为“负面”,然而我们意
识到“
beef
”一词在评论者的地理位置和年龄分组中表示积极意义。如果我们构建
模型来将本书的评论进行分类,我们或许要通过纳入评论者的地理位置和年龄来调
整这一概念偏移,抑或为不同位置和年龄来构建单独的模型。
为检测协变量偏移和标签偏移,我们在模型训练期间计算基线统计。之后,我们可
以使用前述的不同统计学分析距离指标设定阈值,如
KL
,
KS
,
LP
,
L-infinity
范数
以及更多。这些指标解答了关于偏差的若干问题。譬如,散度指标
KL
解答了“不
同产品类别之间星级评分分布有多不同?”而
KS
指标解答了“每个产品类别中,
哪些星级评分导致了最大偏差?”
如果计算出的偏移高于设定的阈值,
SageMaker
可以告警并自动触发一些动作,比
如重新训练。为检测预测标签与某一特征相关的概念偏移,我们使用
SageMaker
模
型监控器(
SageMaker Model Monitor
)来捕捉实时的模型输入和输出,并将模型输
入发送至线下的人力标签工作流来创建符合基本事实的标签。我们使用
SageMaker
Clarify
将捕捉到的模型输出与人类提供的符合基本事实的标签进行比对。如果模型
输出的分布与符合基本事实的标签相差至某一阈值,
SageMaker
可以通知我们,并
自动触发一些动作,例如重新训练。我们将在第
9
章演示如何使用
SageMaker
模型
监控器和
Clarify ...
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Publisher Resources

ISBN: 9787519869663