Salīdzināt metodes

	Risinājums, kas balstīts uz gadījumiem (CBR)	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mīkstā skaitļošana	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1994	1984
Autors	Janet Kolodner; Agnar Aamodt & Enric Plaza (R4 cycle)	Breiman, Friedman, Olshen & Stone
Tips	Experience-based (analogical) problem solving	Recursive partitioning (if-then rules)
Pirmavots	Aamodt, A., & Plaza, E. (1994). Case-based reasoning: Foundational issues, methodological variations, and system approaches. AI Communications, 7(1), 39–59.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Citi nosaukumi	CBR, case-based reasoning cycle, analogy-based reasoning, vaka tabanlı akıl yürütme	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	2	5
Kopsavilkums	Case-based reasoning solves a new problem by retrieving similar problems solved in the past and adapting their solutions, rather than reasoning from first principles or a trained statistical model. Formalized as the Retrieve-Reuse-Revise-Retain cycle by Aamodt and Plaza in 1994 and popularized by Janet Kolodner, CBR mirrors how human experts in medicine, law, and engineering reason by analogy from remembered cases, and it learns simply by storing each newly solved case.	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.