Krahasoni metodat

	Bagim (Agregimi Bootstrap)	AdaBoost	Pemët e vendimmarrjes	Pylli i Rastësishëm
Fusha	Mësimi i makinës	Mësimi i makinës	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning	Machine learning	Machine learning
Viti i origjinës	1996	1997	1984	2001
Krijuesi	Breiman, L.	Freund, Y. & Schapire, R.E.	Breiman, Friedman, Olshen & Stone	Breiman, L.
Lloji	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Ensemble (sequential boosting of weak learners)	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
Burimi themelues	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., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Emërtime të tjera	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Të lidhura	5	5	5	4
Përmbledhja	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.	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.