Sammenlign metoder

	Boosting	Bagging (Bootstrap Aggregating)	Beslutningstre	Gradient Boosting	Random Forest
Fagfelt	Maskinlæring	Maskinlæring	Maskinlæring	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning	Machine learning	Machine learning	Machine learning
Opprinnelsesår	1990–1997	1996	1984	2001	2001
Opphavsperson	Schapire, R. E.; Freund, Y.	Breiman, L.	Breiman, Friedman, Olshen & Stone	Friedman, J. H.	Breiman, L.
Type	Sequential ensemble (iterative reweighting)	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)	Ensemble (bagging of decision trees)
Opprinnelig kilde	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., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relaterte	6	5	5	5	4
Sammendrag	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.	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.	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	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.