August 2024
Beginner to intermediate
640 pages
14h 53m
Korean
Content preview from AI를 위한 필수 수학
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153
Chapter 3 -
데이터에 함수를 최적화시키는 방법
특이도
= =
진짜 음성
음성 레이블 수
진짜 음성
진짜 음성
+
거짓 양성
F
1
점수
이 값은 정밀도와 재현율 점수가 모두 높을 때에만 높다.
AUC
area
under
the
curve
와
ROC
receiver
operating
characteristics
곡선
다양한 임계값에서 분류 모델의 성능을 측정하는 데 사용된다. 두 가지 곡선을 사용하여 특정
변수가 특정 결과를 얼마나 잘 예측하는지 측정한다. 예를 들어 처음 도전한
GRE
과목의 시험
점수로 대학원 진학 시험에 통과할 확률을 얼마나 잘 예측할 수 있는지 측정할 수 있다.
앤드류 응의 저서 『
Machine
Learning
Yearning
』(
2018
)
74
에서 성능 측정 지표에 대한 모
범 사례를 제시한다. 수많은 실험과 성공 및 실패 경험을 기반으로 한 책이기 때문에 실제 인공
지능 애플리케이션을 개발하기 전에 읽어보길 추천한다.
정리하기
지금까지 가장 많이 사용되는 머신러닝 모델 몇 가지를 살펴봤다. 그리고 모델의 수학적 구조
인 훈련 함수, 손실 함수, 최적화를 순서대로 살펴봤다. 우리는 선형 회귀, 로지스틱 회귀, 소프
특이도
specificity
올바르게 분류된 음성 데이터 인스턴스의 비율
F
1
=
2
1
정밀도
1
재현율
+
74
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