方法对比

	AdaBoost	随机森林
领域	机器学习	机器学习
方法族	Machine learning	Machine learning
起源年份	1997	2001
提出者	Freund, Y. & Schapire, R.E.	Breiman, L.
类型	Ensemble (sequential boosting of weak learners)	Ensemble (bagging of decision trees)
开创性文献	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
别名	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
相关	5	4
摘要	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.