Porovnat metody

	Zesilování	Rozhodovací strom	Extra Trees
Obor	Strojové učení	Strojové učení	Strojové učení
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	1990–1997	1984	2006
Tvůrce	Schapire, R. E.; Freund, Y.	Breiman, Friedman, Olshen & Stone	Geurts, P.; Ernst, D.; Wehenkel, L.
Typ	Sequential ensemble (iterative reweighting)	Recursive partitioning (if-then rules)	Ensemble (extremely randomized decision trees)
Původní zdroj	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.	Geurts, P., Ernst, D. & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63(1), 3–42.
Další názvy	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Extremely Randomized Trees, ExtraTreesClassifier, ExtraTreesRegressor, ET
Příbuzné	6	5	5
Shrnutí	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.	Extra Trees (Extremely Randomized Trees), introduced by Geurts, Ernst, and Wehenkel in 2006, is an ensemble of decision trees that pushes randomisation further than Random Forest. Both the candidate features and the split thresholds are chosen completely at random at each node, eliminating the greedy search over thresholds. This extra randomness reduces variance, often matches or exceeds Random Forest accuracy, and runs substantially faster at training time.