方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 反事实解释× | 逻辑回归× | |
|---|---|---|
| 领域≠ | 机器学习 | 研究统计学 |
| 方法族≠ | Machine learning | Process / pipeline |
| 起源年份≠ | 2017 | 1958 |
| 提出者≠ | Sandra Wachter, Brent Mittelstadt & Chris Russell | David Roxbee Cox |
| 类型≠ | Post-hoc, model-agnostic explanation | Method |
| 开创性文献≠ | 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 ↗ |
| 别名≠ | Algorithmic Recourse, Contrastive Explanations, What-If Explanations, Karşıolgusal Açıklamalar | logit model, binomial logistic regression, LR |
| 相关≠ | 2 | 3 |
| 摘要≠ | 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. |
| ScholarGate数据集 ↗ |
|
|