May 2021
Beginner to intermediate
380 pages
9h 22m
Korean
Content preview from 데이터 과학을 위한 통계(2판)
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233
5
장
분류
SS
사이
SS
내부
사이 제곱합의 각 값은 두 그룹 평균 사이의 거리 제곱을 말하며, 내부 제곱합은 공분산행렬에
의해 가중치가 적용된, 각 그룹 내의 평균을 주변으로 퍼져 있는 정도를 나타낸다. 직관적으로,
사이 제곱합을 최대화하고 내부 제곱합을 최소화하는 것이 두 그룹 사이를 가장 명확하게 나누
는 방법이다.
5.2.3
간단한 예
베너블스와 리플리가 저술한 『
Modern
Applied
Statistics
with
S
』(
Springer
,
1994
)와 관
련이 있는
MASS
패키지는
R
에서 사용 가능한
LDA
함수를 제공한다. 아래 예제는 두 예측변수
borrower
_
score
와
payment
_
inc
_
ratio
를 이용해 대출 데이터 표본에 이 함수를 적용하고
선형판별자 가중치를 구한다.
library(MASS)
loan_lda <- lda(outcome ~ borrower_score + payment_inc_ratio,
data=loan3000)
loan_lda$scaling
LD1
borrower_score 7.17583880
payment_inc_ratio -0.09967559
파이썬에서는
sklearn
.
discriminant
_
analysis
의
LinearDiscriminantAnalysis
를 사용
할 수 있다.
scalings
_
property
은
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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.