Сравнение методов

	Байесовский XGBoost	Градиентный бустинг	Случайный лес	XGBoost
Область	Машинное обучение	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning	Machine learning
Год появления	2012–2016	2001	2001	2016
Автор метода	Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization)	Friedman, J. H.	Breiman, L.	Chen, T. & Guestrin, C.
Тип	Ensemble (gradient boosted trees with Bayesian hyperparameter search)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)	Ensemble (gradient-boosted decision trees)
Основополагающий источник	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	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.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Другие названия	Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	XGBoost, extreme gradient boosting, scalable tree boosting
Связанные	4	5	4	5
Сводка	Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches.	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.	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.