May 2018
Beginner to intermediate
672 pages
16h 54m
Korean
Content preview from 핸즈온 머신러닝: 사이킷런과 텐서플로를 활용한 머신러닝, 딥러닝 실무
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242
1
부
머신러닝
7.1
투표 기반 분류기
정확도가
80
%인 분류기 여러 개를 훈련시켰다고 가정합시다. 아마도 로지스틱 회귀 분류기,
SVM
분류기, 랜덤 포레스트 분류기,
K
-
최근접 이웃 분류기 등을 가지고 있을 수 있습니다 (그
림
7
-
1
참조 ).
그림
7-1
여러 분류기 훈련시키기
로지스틱
회귀
SVM
분류기
랜덤 포레스트
분류기 기타...
다양한
예측기들
더 좋은 분류기를 만드는 매우 간단한 방법은 각 분류기의 예측을 모아서 가장 많이 선택된 클
래스를 예측하는 것입니다. 이렇게 다수결 투표로 정해지는 분류기를 직접 투표
hard
voting
분류기
라고 합니다 (그림
7
-
2
).
그림
7-2
직접 투표 분류기의 예측
새로운 인스턴스
앙상블의 예측
(예: 다수결 투표)
예측
다양한
분류기들
243
7
장
앙상블 학습과 랜덤 포레스트
조금 놀랍게도 이 다수결 투표 분류기가 앙상블에 포함된 개별 분류기 중 가장 뛰어난 것보다
도 정확도가 높을 경우가 많습니다. 사실 각 분류기가 약한 학습기
weak
learner
(즉, 랜덤 추측보다
조금 더 높은 성능을 내는 분류기 )일지라도 충분하게 많고 다양하다면
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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.
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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.