Porovnat metody

	Vysvětlitelné extrémní náhodné stromy	Extra Trees	Gradient Boosting
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2006 (Extra Trees); 2017 (SHAP integration)	2006	2001
Tvůrce	Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)	Geurts, P.; Ernst, D.; Wehenkel, L.	Friedman, J. H.
Typ	Ensemble (randomized trees) with post-hoc explainability	Ensemble (extremely randomized decision trees)	Ensemble (sequential boosting of decision trees)
Původní zdroj	Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Další názvy	XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Příbuzné	5	5	5
Shrnutí	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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.	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.