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	Wyjaśnialny Las Losowy	Gradient Boosting
Dziedzina	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning
Rok powstania	2001–2017	2001
Twórca	Breiman, L. (RF); Lundberg & Lee (SHAP attribution)	Friedman, J. H.
Typ	Interpretable ensemble (bagging + post-hoc attribution)	Ensemble (sequential boosting of decision trees)
Źródło pierwotne	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.
Inne nazwy	XRF, interpretable random forest, transparent random forest, random forest with explainability	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Pokrewne	4	5
Podsumowanie	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.