Methoden vergelijken

	LIME: Lokaal interpreteerbare modelonafhankelijke verklaringen	Logistische Regressie
Vakgebied	Machine learning	Onderzoeksstatistiek
Familie	Machine learning	Process / pipeline
Jaar van ontstaan	2016	1958
Grondlegger	Marco Ribeiro, Sameer Singh & Carlos Guestrin	David Roxbee Cox
Type	post-hoc local explanation	Method
Oorspronkelijke bron	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.
Aliassen	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar	logit model, binomial logistic regression, LR
Verwant	2	3
Samenvatting	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.