October 2021
Beginner to intermediate
416 pages
9h 3m
Korean
Content preview from 살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화
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98
살아 움직이는 머신러닝 파이프라인 설계
결과는 [표
4
-
3
]과 같습니다.
표
4-3
학습 데이터셋과 서빙 데이터셋 간의 데이터 드리프트 시각화
피처 이름 간략한 이상치 설명 상세한 이상치 설명
“
company
”
현재 값과 이전 값 사이의
L
-
infinity
distance
가 큼
현재 값과 이전 값 사이의
L
-
infinity
distance
는
0
.
0170752
(최
대
6
자리)이며 임곗값
0
.
01
을 초과함. 오차가 가장 큰 피처는
Experian
.
skew
_
comparator
와
drift
_
comparator
의
L
-
infinity
Norm
은 데이터 입력 파이프라인에
문제가 있음을 알려주는 데이터셋 간의 큰 차이를 보여주는 데 유용합니다.
L
-
infinity
Norm
은 단일 숫자만 반환하므로 스키마가 데이터셋 간의 변동을 탐지하는 데 더 유용하기도 합니다.
4.3.4
편향된 데이터셋
입력 데이터셋에는 편향이라는 잠재적인 문제도 있습니다. 우리는 편향을 현실 세계와 동떨어
진 데이터로 정의합니다. 이는
7
장에서 다룰 공정성
fairness
과는 대조적입니다. 공정성은 서로 다
른 그룹의 사람들에게 이질적인 영향을 미치는 모델의 예측입니다.
편향은 여러 가지 방법으로 데이터에 침투합니다. 데이터셋은 항상 실제 환경의 부분 집합이
며, 우리는 모든 세부 정보를 캡처할
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and much more.
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
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.