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	AdaBoost	Дрво одлучивања	Slučajna šuma
Oblast	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning
Godina nastanka	1997	1984	2001
Tvorac	Freund, Y. & Schapire, R.E.	Breiman, Friedman, Olshen & Stone	Breiman, L.
Tip	Ensemble (sequential boosting of weak learners)	Recursive partitioning (if-then rules)	Ensemble (bagging of decision trees)
Temeljni izvor	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.
Drugi nazivi	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
Srodne	5	5	4
Sažetak	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.