Krahasoni metodat

	Pyllë i Rastësueshëm i Shpjegueshëm	Pemët e vendimmarrjes
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2001–2017	1984
Krijuesi	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Breiman, Friedman, Olshen & Stone
Lloji	Interpretable ensemble (bagging + post-hoc attribution)	Recursive partitioning (if-then rules)
Burimi themelues	Lundberg, S. M., & Lee, S.-I. (2017). A unified approach to interpreting model predictions. Advances in Neural Information Processing Systems, 30, 4765–4774.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Emërtime të tjera	XRF, interpretable random forest, transparent random forest, random forest with explainability	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Të lidhura	4	5
Përmbledhja	Explainable Random Forest (XRF) combines the predictive power of Breiman's Random Forest ensemble with systematic post-hoc attribution methods — principally SHAP values and mean-decrease-in-impurity importance — to make model decisions transparent and auditable. It delivers both high accuracy and human-interpretable feature contributions, satisfying demands from regulators, domain experts, and academic reviewers alike.	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.