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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253
7
장
앙상블 학습과 랜덤 포레스트
그림
7-6
(랜덤 포레스트 분류기에서 얻은 )
MNIST
픽셀 중요도
매우 중요함
중요하지 않음
랜덤 포레스트는 특히 특성을 선택해야 할 때 어떤 특성이 중요한지 빠르게 확인할 수 있어 매
우 편리합니다.
7.5
부스팅
부스팅
boosting
(원래는 가설 부스팅
hypothesis
boosting
이라 불렀습니다 )은 약한 학습기를 여러 개
연결하여 강한 학습기를 만드는 앙상블 방법을 말합니다. 부스팅 방법의 아이디어는 앞의 모델
을 보완해나가면서 일련의 예측기를 학습시키는 것입니다. 부스팅 방법에는 여러 가지가 있지
만 가장 인기 있는 것은 아다부스트
AdaBoost
19
(
Adaptive
Boosting
의 줄임말 )와 그래디언트
부스팅
Gradient
Boosting
입니다. 아다부스트부터 시작해보죠.
7.5.1
아다부스트
이전 예측기를 보완하는 새로운 예측기를 만드는 방법은 이전 모델이 과소적합했던 훈련 샘플
19
「
A
Decision
-
Theoretic
Generalization
of
On
-
Line
Learning
and
an
Application
to
Boosting
」 ,
Yoav
Freund
,
Robert
E
.
Schapire
(
1997
),
http
://
goo
.
gl
/
OIduRW
254
1
부
머신러닝
의 가중치를 더 높이는 것입니다. ...
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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.