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머신러닝 인과분석
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머신러닝 인과분석

  • 김양석
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  • 박영사
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  • 2023-08-30 출간
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  • 239페이지
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  • 171 X 244 X 6mm
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  • ISBN 9791130318189
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북스크린영상으로 만나보는 한 권의 책 이야기

목차

Chapter 00 서장
저술 목적··················································· 10
운영시스템················································· 14
개발 환경··················································· 14

Chapter 01 가설 검정
서론·························································· 26
가설과 가설 검정·········································· 26
가설······················································· 26
가설 검정················································ 27
검증의 단계················································ 28
1단계: 귀무가설 및 대립가설 설명················· 28
2단계: 데이터 수집···································· 29
3단계: 통계 테스트 수행······························ 29
4단계: 귀무가설 기각 여부 결정···················· 30
5단계: 연구 결과 제시································ 30
검증 오류················································ 31
가설 검정 사례············································ 32
데이터 로드············································· 32
정규성 검증에 대한 가설 검정······················· 34
상관성 검증에 대한 가설 검정······················· 36
모수 통계 가설 검정··································· 39
비모수 통계 가설 검정································ 44
결론·························································· 48

Chapter 02 선형 회귀 모델링
서론·························································· 50
모델과 모델링············································· 50
데이터셋···················································· 51
단순 회귀 분석············································ 54
가설설정················································· 54
모델링···················································· 54
모델링 결과············································· 54
AIC······················································· 60
다중 회귀 분석············································ 82
모델링···················································· 88
모델링 결과············································· 88
회귀 모델 가정 검정··································· 92
결론·························································104
Chapter 03 이산 회귀 모델링
서론·························································106
모델링 기법···············································106
로짓(Logit) 모형····································107
프로빗 모형···········································107
로짓과 프로빗 모형의 차이점······················108
데이터 분석 사례·········································109
Step 1: 라이브러리 가져오기·····················109
Step 2: 데이터 로딩 및 이해······················109
Step 3: 가설 설정···································110
Step 4: 데이터 준비································110
Step 5: Logit 모델링·······························113
Step 6: Probit 모델링·····························116
결론·························································118

Chapter 04 인과 추론 분석
서론·························································120
인과 추론의 4 단계······································121
모델에서 목표 추정치 식별·························124
확인된 추정치를 기반으로 인과 추론·············125
획득한 추정치에 대한 반박·························126
DoWhy 인과 추론의 특징·····························128
명시적 식별 가능·····································128
식별과 추정의 분리··································128
자동화된 견고성 검사·······························128
확장성··················································129
인과 추론 분석 사례 - 호텔 예약 취소···············129
Step 1: 라이브러리 가져오기·····················130
Step 2: 데이터 로딩 및 데이터 이해·············130
Step 3: 데이터 준비································133
Step 4: DoWhy를 활용한 인과 관계 추정····142
결론·························································151

Chapter 05 인과 발견 분석
서론·························································154
패키지 설치···············································154
분석 방법 이해···········································155
Step 1: 라이브러리 가져오기·····················156
Step 2: 검증 데이터 생성··························156
Step 3: 인과 관계 발견····························158
Step 4: 오차 변수 간의 독립성 검증·············159
분류 문제의 인과 발견··································160
Step 1: 라이브러리 가져오기·····················160
Step 2: 커스텀 함수 만들기·······················161
Step 3: 데이터 로딩하기···························161
Step 4: 모델링 하기································162
Step 5: 변수 오차 간 독립성 검증···············165
Step 6: 예측 모델 생성과 예측 영향도 분석···166
수치 예측 문제의 인과 발견····························167
Step 1: 라이브러리 가져오기·····················167
Step 2: 커스텀 함수 만들기·······················168
Step 3: 데이터 로딩하기···························168
Step 4: 모델링 하기································170
Step 5: 변수 오차 간 독립성 검증··············· 172
Step 6: 예측 모델 생성과 예측 영향도 분석··· 172
Step 7: 최적 개입의 추정·························· 173
결론·························································174

Chapter 06 인과 영향 분석
서론·························································176
Causal Impact··········································177
모델의 동작 방식의 이해···························· 179
폭스바겐 인과 영향 분석 사례·························188
Step 1: 라이브러리 로딩··························· 188
Step 2: 데이터 로딩 및 데이터 이해············· 189
Step 3: 기본 모델 분석···························· 191
Step 4: 시계열 성분 분해·························· 195
Step 5: 사용자 정의 모델·························· 197
결론·························································202

Chapter 07 반대사실 분석
서론·························································206
소득 분류 반대사실 분석·······························207
Step 1: 라이브러리 가져오기····················· 207
Step 2: 데이터셋 로딩 및 이해··················· 207
Step 3: DiCE로 카운터 팩트 생성··············· 209
Step 4: 카운터 팩츄얼 사례 기반 속성 중요도··· 216
주택 가격 예측 반대사실 분석 사례··················219
Step 1: 라이브러리 로딩···························219
Step 2: 데이터 로딩 및 이해······················220
Step 3: DiCE로 카운터 팩트 생성···············223
Step 4: 카운터 팩츄얼 기반 속성 중요도·······225
결론·························································228
참고문헌···················································229


색인·························································232
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