Salīdzināt metodes

	Izskaidrojamie papildu koki	Gradient Boosting	Random Forest
Nozare	Mašīnmācīšanās	Mašīnmācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning	Machine learning
Izcelsmes gads	2006 (Extra Trees); 2017 (SHAP integration)	2001	2001
Autors	Geurts, P., Ernst, D., Wehenkel, L. (Extra Trees); Lundberg, S. M. (SHAP explainability layer)	Friedman, J. H.	Breiman, L.
Tips	Ensemble (randomized trees) with post-hoc explainability	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Pirmavots	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Citi nosaukumi	XAI-ET, Explainable ET, Interpretable Extra Trees, Extra Trees with SHAP	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Saistītās	5	5	4
Kopsavilkums	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.	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.