方法对比

	装袋集成	梯度提升（Gradient Boosting）	多数表决
领域	集成学习	机器学习	集成学习
方法族	Machine learning	Machine learning	Machine learning
起源年份	1996	2001	1996
提出者	Leo Breiman	Friedman, J. H.	Leo Breiman
类型	parallel ensemble	Ensemble (sequential boosting of decision trees)	voting aggregation
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. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
别名	bootstrap aggregating	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	hard voting
相关	4	5	5
摘要	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	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.	Majority voting is an ensemble method that combines predictions from multiple base classifiers by selecting the class that receives the most votes. Each base classifier casts one vote for a predicted class, and the final prediction is the class with the majority (plurality). This approach was formalized by Leo Breiman and colleagues in the 1990s as a simple yet effective way to improve classification accuracy.