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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106
파이썬 라이브러리를 활용한 머신러닝(번역개정판)
결정 트리의 복잡도 제어하기
일반적으로 트리 만들기를 모든 리프 노드가 순수 노드가 될 때까지 진행하면 모델이 매우 복
잡해지고 훈련 데이터에 과대적합됩니다. 순수 노드로 이루어진 트리는 훈련 세트에
100
% 정
확하게 맞는다는 의미입니다. 즉 훈련 세트의 모든 데이터 포인트는 정확한 클래스의 리프 노
드에 있습니다. [그림
2
-
26
]의 왼쪽 그래프가 과대적합된 것으로 볼 수 있습니다. 클래스
0
으
로 결정된 영역이 클래스
1
에 속한 포인트들로 둘러쌓인 것을 볼 수 있습니다. 그 반대 모습도
보입니다. 이는 바람직한 결정 경계의 모습이 아닙니다. 결정 경계가 클래스의 포인트들에서
멀리 떨어진 이상치
outlier
하나에 너무 민감하기 때문입니다.
과대적합을 막는 전략은 크게 두 가지입니다. 트리 생성을 일찍 중단하는 전략 (
사전 가지치기
pre
-
pruning
)과 트리를 만든 후 데이터 포인트가 적은 노드를 삭제하거나 병합하는 전략입니다
(
사후 가지치기
post
-
pruning
또는 그냥
가지치기
pruning
). 사전 가지치기 방법은 트리의 최대 깊이나
리프의 최대 개수를 제한하거나, 또는 노드가 분할하기 위한 포인트의 최소 개수를 지정하는
것입니다.
scikit
-
learn
에서 결정 트리는
DecisionTreeRegressor ...
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and much more.
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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.