June 2024
Beginner to intermediate
352 pages
9h 29m
Korean
Content preview from 개발자를 위한 필수 수학
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219
6
장
로지스틱 회귀와 분류
6
장
로지스틱 회귀와 분류
이 장에서는 하나 이상의 독립 변수가 주어졌을 때 결과의 확률을 예측하는
로지스틱 회귀
logistic
regression
를 다룹니다. 로지스틱 회귀는 선형 회귀처럼 실수를 예측하는 것이 아니라 범주를 예측
하는
분류 알고리즘
입니다.
1
모든 변수를 무한한 실숫값을 나타낼 수 있는 연속형
continuous
으로 표현할 수 있는 것은 아닙니
다. 변수를 이산형
discrete
이나 정수 또는 불리언 (
1
/
0
, 참/거짓)으로 표현하는 것이 더 나은 경우
가 있습니다. 로지스틱 회귀는 이산형 (이진수
1
또는
0
) 또는 범주형 (정수)인 출력 변수를 위
해 훈련됩니다. 이 모델은 확률 형태의 연속형 값을 출력하지만 임곗값을 사용해 이산형 값으
로 변환할 수 있습니다.
로지스틱 회귀는 구현하기 쉽고 이상치와 기타 데이터 문제에 상당히 탄력적입니다. 로지스틱
회귀를 사용하면 많은 머신러닝 문제를 잘 해결할 수 있으며, 다른 유형의 지도 학습보다 실용
성과 성능이 뛰어납니다.
5
장에서 선형 회귀를 다뤘을 때와 마찬가지로, 통계와 머신러닝 영역의 도구와 분석을 사용해
두 분야의 경계를 넘나들려고 합니다. 로지스틱 회귀는 확률에서 선형 회귀에 이르기까지 이
책에서 배운 많은 개념을 통합합니다.
1
옮긴이_ 이따금 로지스틱 회귀를 ...
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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.