방법 비교

	다수결 투표	AdaBoost
분야	앙상블 학습	머신러닝
계열	Machine learning	Machine learning
기원 연도	1996	1997
창시자	Leo Breiman	Freund, Y. & Schapire, R.E.
유형	voting aggregation	Ensemble (sequential boosting of weak learners)
원전	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	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.
별칭	hard voting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
관련	5	5
요약	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.	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.