Módszerek összehasonlítása

	Magyarázható Random Forest	Gradient Boosting	Véletlen erdő
Tudományterület	Gépi tanulás	Gépi tanulás	Gépi tanulás
Módszercsalád	Machine learning	Machine learning	Machine learning
Keletkezés éve	2001–2017	2001	2001
Megalkotó	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Friedman, J. H.	Breiman, L.
Típus	Interpretable ensemble (bagging + post-hoc attribution)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging 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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alternatív nevek	XRF, interpretable random forest, transparent random forest, random forest with explainability	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Kapcsolódó	4	5	4
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.	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.	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.