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	Cây Quyết định	LIME: Giải thích Mô hình Cục bộ Có thể Diễn giải và Độc lập Mô hình
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1984	2016
Người khởi xướng	Breiman, Friedman, Olshen & Stone	Marco Ribeiro, Sameer Singh & Carlos Guestrin
Loại	Recursive partitioning (if-then rules)	post-hoc local explanation
Công trình gốc	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). "Why should I trust you?": Explaining the predictions of any classifier. ACM SIGKDD, 1135–1144.
Tên gọi khác	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Local Surrogate Explanations, Model-Agnostic Local Explanations, Locally Faithful Approximations, Yerel Yorumlanabilir Model-Bağımsız Açıklamalar
Liên quan	5	2
Tóm tắt	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.