Uporedite metode

	Екстремно рандомизована стабла са објашњењем (Explainable Extra Trees)	XGBoost
Oblast	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning
Godina nastanka	2006 (Extra Trees); 2017 (SHAP integration)	2016
Tvorac	Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)	Chen, T. & Guestrin, C.
Tip	Ensemble (randomized trees) with post-hoc explainability	Ensemble (gradient-boosted decision trees)
Temeljni izvor	Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Drugi nazivi	XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP	XGBoost, extreme gradient boosting, scalable tree boosting
Srodne	5	5
Sažetak	Explainable Extra Trees combines the Extremely Randomized Trees (Extra Trees) ensemble algorithm with post-hoc explainability methods — most commonly SHAP values — to deliver both strong predictive performance and transparent, feature-level explanations. It extends the classic Extra Trees classifier or regressor so that every prediction can be decomposed into individual feature contributions, satisfying demands for accountability in applied and regulated domains.	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.