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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281
8
장
차원 축소
8.3.6
적절한 차원 수 선택하기
축소할 차원 수를 임의로 정하기보다는 충분한 분산 (예를 들면
95
%)이 될 때까지 더해야 할
차원 수를 선택하는 쪽을 더 선호합니다. 물론 데이터 시각화를 위해 차원을 축소하는 경우에는
차원을
2
개나
3
개로 줄이는 것이 일반적입니다.
다음 코드는 차원을 축소하지 않고
PCA
를 계산한 뒤 훈련 세트의 분산을
95
%로 유지하는 데
필요한 최소한의 차원 수를 계산합니다.
18
pca
=
PCA
()
pca
.
fit
(
X
_
train
)
cumsum
=
np
.
cumsum
(
pca
.
explained
_
variance
_
ratio
_
)
d
=
np
.
argmax
(
cumsum
>
=
0
.
95
)
+
1
그런 다음
n
_
components
=
d
로 설정하여
PCA
를 다시 실행합니다. 하지만 유지하려는 주성분의
수를 지정하기보다는 보존하려는 분산의 비율을
n
_
components
에
0
.
0
에서
1
.
0
사이로 설정하
는 편이 훨씬 낫습니다.
pca
=
PCA
(
n
_
components
=
0
.
95
)
X
_
reduced
=
pca
.
fit
_
transform
(
X
_
train
)
또 다른 방법은 설명된 분산을 차원 수에 대한 함수로 그리는 것입니다(그냥
cumsum
을 그래프
로 그리면 됩니다. [그림
8
-
8
]을 보세요 ). 일반적으로 이 그래프에는 설명된 분산의 빠른 성장
이 ...
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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.