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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263
7
장
앙상블 학습과 랜덤 포레스트
NOTE
_
그래디언트 부스팅에 다른 비용 함수를 사용할 수도 있습니다.
loss
매개변수를 이용해 지정합니다
(자세한 내용은 사이킷런 문서를 참고하세요 ).
31
7.6
스태킹
31
이 장에서 이야기할 마지막 앙상블 모델은 스태킹
stacking
32
(
stacked
generalization
의 줄임
말)입니다. 이는 ‘앙상블에 속한 모든 예측기의 예측을 취합하는 간단한 함수(직접 투표 같은 )
를 사용하는 대신 취합하는 모델을 훈련시킬 수 없을까요?’라는 기본 아이디어로 출발합니다.
[그림
7
-
12
]는 새로운 샘플에 회귀 작업을 수행하는 앙상블을 보여주고 있습니다. 아래의 세
예측기는 각각 다른 값(
3
.
1
,
2
.
7
,
2
.
9
)을 예측하고 마지막 예측기(블렌더
blender
또는 메타 학
습기
meta
learner
라고 합니다 )가 이 예측을 입력으로 받아 최종 예측 (
3
.
0
)을 만듭니다.
그림
7-12
블렌딩 예측기를 사용한 예측 취합
블렌딩
예측 결과
예측
새로운 샘플
31
옮긴이_
GradientBoostingClassifier
의
loss
매개변수 옵션은 로지스틱 손실 함수를 의미하는 ‘
deviance
’가 기본값이고
아다부스트에서 사용하는 ‘
exponential
’도 있습니다.
GradientBoostingRegressor
의
loss
매개변수 ...
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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.