방법 비교

	AdaBoost	다수결 투표	랜덤 포레스트
분야	머신러닝	앙상블 학습	머신러닝
계열	Machine learning	Machine learning	Machine learning
기원 연도	1997	1996	2001
창시자	Freund, Y. & Schapire, R.E.	Leo Breiman	Breiman, L.
유형	Ensemble (sequential boosting of weak learners)	voting aggregation	Ensemble (bagging of decision trees)
원전	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. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
별칭	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	hard voting	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
관련	5	5	4
요약	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.	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.	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.