Vertaile menetelmiä

	Majority Voting	AdaBoost	Bagging-ensemble
Tieteenala	Yhdistelmäoppiminen	Koneoppiminen	Yhdistelmäoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning
Syntyvuosi	1996	1997	1996
Kehittäjä	Leo Breiman	Freund, Y. & Schapire, R.E.	Leo Breiman
Tyyppi	voting aggregation	Ensemble (sequential boosting of weak learners)	parallel ensemble
Alkuperäislähde	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.	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.
Rinnakkaisnimet	hard voting	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	bootstrap aggregating
Liittyvät	5	5	4
Tiivistelmä	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.	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.