Methoden vergleichen

	LIME: Local Interpretable Model-agnostic Explanations	Logistische Regression
Fachgebiet	Maschinelles Lernen	Forschungsstatistik
Familie	Machine learning	Process / pipeline
Entstehungsjahr	2016	1958
Urheber	Marco Ribeiro, Sameer Singh & Carlos Guestrin	David Roxbee Cox
Typ	post-hoc local explanation	Method
Wegweisende Quelle	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Aliasnamen	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar	logit model, binomial logistic regression, LR
Verwandt	2	3
Zusammenfassung	LIME, introduced by Ribeiro, Singh, and Guestrin in 2016, explains the predictions of any black-box classifier or regressor by building a simple, locally faithful surrogate model around a single prediction of interest. Rather than explaining the global model, LIME focuses on why a specific instance was classified the way it was, making complex models such as deep neural networks and ensemble methods interpretable to end-users, domain experts, and auditors.	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.