विधियों की तुलना करें

	प्रति-तथ्यात्मक स्पष्टीकरण	लॉजिस्टिक रिग्रेशन
क्षेत्र	मशीन अधिगम	अनुसंधान सांख्यिकी
परिवार	Machine learning	Process / pipeline
उद्भव वर्ष	2017	1958
प्रवर्तक	Sandra Wachter, Brent Mittelstadt & Chris Russell	David Roxbee Cox
प्रकार	Post-hoc, model-agnostic explanation	Method
मौलिक स्रोत	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
उपनाम	Algorithmic Recourse, Contrastive Explanations, What-If Explanations, Karşıolgusal Açıklamalar	logit model, binomial logistic regression, LR
संबंधित	2	3
सारांश	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.