May 2020
Beginner to intermediate
952 pages
24h 11m
Korean
Content preview from 핸즈온 머신러닝(2판): 사이킷런, 케라스, 텐서플로 2를 활용한 머신러닝, 딥러닝 완벽 실무
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153
3
장
분류
다중 레이블 분류기를 평가하는 방법은 많습니다. 적절한 지표는 프로젝트에 따라 다릅니다. 예
를 들어 각 레이블의
F
1
점수를 구하고 (또는 앞서 언급한 어떤 이진 분류 지표를 사용하여 ) 간
단하게 평균 점수를 계산합니다. 다음 코드는 모든 레이블에 대한
F
1
점수의 평균을 계산합니다.
>>> y_train_knn_pred = cross_val_predict(knn_clf, X_train, y_multilabel, cv=3)
>>> f1_score(y_multilabel, y_train_knn_pred, average="macro")
0.976410265560605
실제로는 아닐 수 있지만 이 코드는 모든 레이블의 가중치가 같다고 가정한 것입니다. 특히 앨
리스 사진이 밥이나 찰리 사진보다 훨씬 많다면 앨리스 사진에 대한 분류기의 점수에 더 높은 가
중치를 둘 것입니다. 간단한 방법은 레이블에 클래스의
지지도
support
(즉, 타깃 레이블에 속한
샘플 수 )를 가중치로 주는 것입니다. 이렇게 하려면 이전 코드에서
average
=
"
weighted
"
로
설정하면 됩니다.
18
3.7
다중 출력 분류
마지막으로 알아볼 분류 작업은
다중 출력 다중 클래스 분류
multioutput
-
multiclass
classification
(또는 간단
히
다중 출력 분류
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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.