Compară metode

	AdaBoost	Bagging Ensemble	Pădurea Aleatoare (Random Forest)
Domeniu	Învățare automată	Învățare prin ansambluri	Învățare automată
Familie	Machine learning	Machine learning	Machine learning
Anul apariției	1997	1996	2001
Autorul original	Freund, Y. & Schapire, R.E.	Leo Breiman	Breiman, L.
Tip	Ensemble (sequential boosting of weak learners)	parallel ensemble	Ensemble (bagging of decision trees)
Sursa seminală	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.
Denumiri alternative	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	bootstrap aggregating	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Înrudite	5	4	4
Rezumat	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.	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.