May 2022
Beginner to intermediate
448 pages
13h 7m
Korean
book
밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
by Joel Grus, Hangyeol Kim, Seongju Ha, Eunjung Park
Content preview from 밑바닥부터 시작하는 데이터 과학 데이터 분석을 위한 파이썬 프로그래밍과 수학·통계 기초
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Data Science
234
이를 보통 커널 트릭(
kernel
trick
)이라고 표현한다. 데이터 포인트를 더 높은 차
원의 공간으로 직접 매핑하기보다는(데이터가 많거나 매핑이 복잡하면 계산량
이 무척 많을 수 있기 때문에) ‘커널(
kernel
) 함수’로 더 높은 차원에서의 내적을
계산하고 그것으로 초평면을 찾는다.
최적화 코드를 제대로 작성할 줄 아는 전문가에게 의지하지 않고 직접 서포트
벡터 머신을 작성하는 것은 굉장히 힘든 일일뿐더러 별로 좋은 생각이 아닐 수
있으니, 서포트 벡터 머신에 대한 논의는 이쯤에서 마무리하도록 하자.
16.6
더 공부해 보고 싶다면
·
scikit
-
learn
에는 로지스틱 회귀 분석
1
과 서포트 벡터 머신
2
을 위한 모델이 모
두 구현되어 있다.
·
LIBSVM
3
은
scikit
-
learn
의 서포트 벡터 머신 모듈이 쓰고 있는 구현체다. 이
웹페이지에는 서포트 벡터 머신에 대한 유용한 문서가 많다.
1
https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression
2
https://scikit-learn.org/stable/modules/svm.html
3
https://www.csie.ntu.edu.tw/~cjlin/libsvm/
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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.