Salīdzināt metodes

	Skaidrojams lēmumu koks	Koku lēmumu pieņemšana (Decision Tree)
Nozare	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1984 (CART); XAI framing formalized 2010s–2020s	1984
Autors	Breiman, L.; Friedman, J.; Olshen, R. A.; Stone, C. J.	Breiman, Friedman, Olshen & Stone
Tips	Interpretable supervised learning model	Recursive partitioning (if-then rules)
Pirmavots	Breiman, L., Friedman, J., Olshen, R. A., & Stone, C. J. (1984). Classification and Regression Trees. Wadsworth & Brooks/Cole. ISBN: 978-0-412-04841-8	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Citi nosaukumi	XDT, interpretable decision tree, rule-based decision tree, transparent decision tree	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Saistītās	4	5
Kopsavilkums	An Explainable Decision Tree is a classification or regression tree deliberately grown to be shallow, readable, and auditable — producing a finite set of if-then rules that a human can verify without additional tools. It sits at the intersection of predictive modelling and Explainable AI (XAI), chosen when stakeholders must understand and trust every prediction the model makes.	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.