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| 반사실적 설명× | 로지스틱 회귀× | |
|---|---|---|
| 분야≠ | 머신러닝 | 연구 통계 |
| 계열≠ | Machine learning | Process / pipeline |
| 기원 연도≠ | 2017 | 1958 |
| 창시자≠ | Sandra Wachter, Brent Mittelstadt & Chris Russell | David Roxbee Cox |
| 유형≠ | Post-hoc, model-agnostic explanation | Method |
| 원전≠ | Wachter, S., Mittelstadt, B., & Russell, C. (2017). Counterfactual explanations without opening the black box: Automated decisions and the GDPR. Harvard Journal of Law & Technology, 31, 841–887. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| 별칭≠ | Algorithmic Recourse, Contrastive Explanations, What-If Explanations, Karşıolgusal Açıklamalar | logit model, binomial logistic regression, LR |
| 관련≠ | 2 | 3 |
| 요약≠ | Counterfactual explanations, introduced by Wachter, Mittelstadt, and Russell in 2017, answer the question: 'What is the smallest change to the input that would have produced a different model output?' Rather than explaining why a model made a decision, they describe what would need to change for that decision to be reversed, making them particularly valuable for high-stakes applications such as credit scoring, medical diagnosis, and hiring decisions under frameworks like the EU GDPR. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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