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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250
1
부
머신러닝
두 샘플링하는 것을 랜덤 패치 방식
Random
Patches
method
이라고 합니다.
9
훈련 샘플을 모두 사
용하고 (즉,
bootstrap
=
False
이고
max
_
samples
=
1
.
0
) 특성은 샘플링하는 (즉,
bootstrap
_
features
=
True
이고
max
_
features
는
1
.
0
보다 작은 ) 것을 랜덤 서브스페이스 방식
Random
Subspaces
method
이라고 합니다.
10
특성 샘플링은 더 다양한 예측기를 만들며 편향을 늘리는 대신 분산을 낮춥니다.
7.4
랜덤 포레스트
앞서 언급했듯이 랜덤 포레스트
11
는 일반적으로 배깅 방법(또는 페이스팅)을 적용한 결정 트
리의 앙상블입니다. 전형적으로
max
_
samples
를 훈련 세트의 크기로 지정합니다.
Bagging
Classifier
에
DecisionTreeClassifier
를 넣어 만드는 대신 결정 트리에 최적화되어 사용
하기 편리한
RandomForestClassifier
를 사용할 수 있습니다 (비슷하게 회귀 문제를 위한
RandomForestRegressor
가 있습니다 ).
12
다음은 (최대
16
개의 리프 노드를 갖는 )
500
개 트
리로 이뤄진 랜덤 포레스트 분류기를 여러
CPU
코어에서
13
훈련시키는 코드입니다.
from sklearn
.
ensemble import RandomForestClassifier ...
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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.