Módszerek összehasonlítása

	Magyarázható Random Forest	Döntési fa	Gradient Boosting
Tudományterület	Gépi tanulás	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	2001–2017	1984	2001
Megalkotó	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Breiman, Friedman, Olshen & Stone	Friedman, J. H.
Típus	Interpretable ensemble (bagging + post-hoc attribution)	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)
Alapmű	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alternatív nevek	XRF, interpretable random forest, transparent random forest, random forest with explainability	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Kapcsolódó	4	5	5
Összefoglaló	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.