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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285
8
장
차원 축소
rnd
_
pca
=
PCA
(
n
_
components
=
154
,
svd
_
solver
=
"
randomized
")
X
_
reduced
=
rnd
_
pca
.
fit
_
transform
(
X
_
train
)
8.4
커널
PCA
5
장에서 샘플을 매우 높은 고차원 공간 (특성 공간
feature
space
)으로 암묵적으로 매핑하여 서포트
벡터 머신의 비선형 분류와 회귀를 가능하게 하는 수학적 기법인 커널 트릭에 대해 이야기했습
니다. 고차원 특성 공간에서의 선형 결정 경계는 원본 공간에서는 복잡한 비선형 결정 경계에 해
당한다는 것을 배웠습니다.
같은 기법을
PCA
에 적용해 복잡한 비선형 투영으로의 차원 축소를 가능하게 할 수 있습니다.
이를 커널
PCA
kernel
PCA
(
kPCA
)라고 합니다.
23
이 기법은 투영된 후에 샘플의 군집을 유지하
거나 꼬인 매니폴드에 가까운 데이터셋을 펼칠 때도 유용합니다.
예를 들어 다음 코드는 사이킷런의
KernelPCA
를 사용해
RBF
커널로
kPCA
를 적용하고 있습
니다 (
RBF
커널과 다른 커널들에 대한 자세한 내용은
5
장을 참조하세요 ).
from sklearn
.
decomposition import KernelPCA
rbf
_
pca
=
KernelPCA
(
n
_
components
=
2
,
kernel
=
"
rbf
",
gamma
=
0
.
04
)
X
_
reduced ...
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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.