Сравнение методов

	Байесовский LightGBM	Байесовский XGBoost	Случайный лес
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	2017 (LightGBM); 2012 (Bayesian optimization)	2012–2016	2001
Автор метода	Ke et al. (LightGBM); Snoek et al. (Bayesian optimization)	Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization)	Breiman, L.
Тип	Gradient boosting with Bayesian hyperparameter search	Ensemble (gradient boosted trees with Bayesian hyperparameter search)	Ensemble (bagging of decision trees)
Основополагающий источник	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. In Advances in Neural Information Processing Systems, 30, 3146–3154.	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.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Другие названия	Bayesian-tuned LightGBM, LightGBM + Bayesian optimization, BayesOpt LightGBM, LightGBM with BayesOpt	Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Связанные	5	4	4
Сводка	Bayesian LightGBM combines LightGBM — a highly efficient histogram-based gradient boosting framework — with Bayesian hyperparameter optimization. Instead of exhaustive grid search or random search, a probabilistic surrogate model guides the search for optimal hyperparameters, dramatically reducing the number of costly model evaluations needed to reach strong predictive performance.	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.	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.