April 2019
Beginner to intermediate
480 pages
12h 34m
Korean
Content preview from 파이썬 라이브러리를 활용한 머신러닝(번역개정판): 사이킷런 핵심 개발자가 쓴 머신러닝과 데이터 과학 실무서
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2
장
지도 학습
125
learn
의 그레이디언트 부스팅 구현보다 빨랐습니다 (그리고 튜닝하기도 쉽습니다 ).
장단점과 매개변수
그레이디언트 부스팅 결정 트리는 지도 학습에서 가장 강력하고 널리 사용하는 모델 중 하나입니
다. 가장 큰 단점은 매개변수를 잘 조정해야 한다는 것과 훈련 시간이 길다는 것입니다. 다른 트
리 기반 모델처럼 특성의 스케일을 조정하지 않아도 되고 이진 특성이나 연속적인 특성에서도 잘
동작합니다. 그리고 트리 기반 모델의 특성상 희소한 고차원 데이터에는 잘 작동하지 않습니다.
그레이디언트 부스팅 트리 모델의 중요 매개변수는 트리의 개수를 지정하는
n
_
estimators
와
이전 트리의 오차를 보정하는 정도를 조절하는
learning
_
rate
입니다. 이 두 매개변수는 매우
깊게 연관되며
learning
_
rate
를 낮추면 비슷한 복잡도의 모델을 만들기 위해서 더 많은 트리
를 추가해야 합니다.
n
_
estimators
가 클수록 좋은 랜덤 포레스트와는 달리 그레이디언트 부스
팅에서
n
_
estimators
를 크게 하면 모델이 복잡해지고 과대적합될 가능성이 높아집니다. 일반
적인 관례는 가용한 시간과 메모리 한도에서
n
_
estimators
를 맞추고 나서 적절한
learning
_
rate
를 찾는 것입니다.
scikit
-
learn
0
.
20
버전에서는
GradientBoostingClassifier ...
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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.