Salīdzināt metodes

	Koku lēmumu pieņemšana (Decision Tree)	LIME: Lokāli interpretējamas modeļu neatkarīgas skaidrojumu metodes
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1984	2016
Autors	Breiman, Friedman, Olshen & Stone	Marco Ribeiro, Sameer Singh & Carlos Guestrin
Tips	Recursive partitioning (if-then rules)	post-hoc local explanation
Pirmavots	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.
Citi nosaukumi	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
Saistītās	5	2
Kopsavilkums	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.