Compară metode

	Pădurea Aleatorie Explicabilă	Arbore de decizie	Gradient Boosting	XGBoost
Domeniu	Învățare automată	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Anul apariției	2001–2017	1984	2001	2016
Autorul original	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Breiman, Friedman, Olshen & Stone	Friedman, J. H.	Chen, T. & Guestrin, C.
Tip	Interpretable ensemble (bagging + post-hoc attribution)	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)	Ensemble (gradient-boosted decision trees)
Sursa seminală	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Denumiri alternative	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	XGBoost, extreme gradient boosting, scalable tree boosting
Înrudite	4	5	5	5
Rezumat	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.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.