方法对比

	Bagging（Bootstrap Aggregating）	梯度提升（Gradient Boosting）	随机森林
领域	机器学习	机器学习	机器学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	1996	2001	2001
提出者	Breiman, L.	Friedman, J. H.	Breiman, L.
类型	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
开创性文献	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	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.
别名	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	5	5	4
摘要	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.