Usporedite metode

	Stablo odlučivanja	Generalizirani aditivni model (GAM)	Povećanje gradijenta
Područje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	1984	1986	2001
Tvorac	Breiman, Friedman, Olshen & Stone	Trevor Hastie & Robert Tibshirani	Friedman, J. H.
Vrsta	Recursive partitioning (if-then rules)	Semi-parametric additive regression model	Ensemble (sequential boosting of decision trees)
Temeljni izvor	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Hastie, T., & Tibshirani, R. (1986). Generalized additive models. Statistical Science, 1(3), 297–310.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Drugi nazivi	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Srodne	5	4	5
Sažetak	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.	A generalized additive model, introduced by Trevor Hastie and Robert Tibshirani in 1986, extends the generalized linear model by replacing each linear term with a smooth, data-driven function of the predictor. This lets the model capture nonlinear relationships while preserving the additive, term-by-term interpretability of regression: each predictor contributes its own estimated curve, and the curves simply add up (on a link scale) to predict the response.	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.