Σύγκριση μεθόδων

	LIME: LIME: Τοπικές Ερμηνεύσιμες Ανεξάρτητες Μοντέλων Εξηγήσεις	Naive Bayes
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	2016	1997
Δημιουργός	Marco Ribeiro, Sameer Singh & Carlos Guestrin	Mitchell, T. M. (textbook treatment)
Τύπος	post-hoc local explanation	Probabilistic classifier (Bayes' theorem with conditional independence)
Θεμελιώδης πηγή	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.	Mitchell, T. M. (1997). Machine Learning. McGraw-Hill. ISBN: 978-0070428072
Εναλλακτικές ονομασίες	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar	Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes
Συναφείς	2	4
Σύνοψη	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.	Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.