Vertaile menetelmiä

	Yleistetty additiivinen malli (GAM)	Gradient Boosting
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	1986	2001
Kehittäjä	Trevor Hastie & Robert Tibshirani	Friedman, J. H.
Tyyppi	Semi-parametric additive regression model	Ensemble (sequential boosting of decision trees)
Alkuperäislähde	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.
Rinnakkaisnimet	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Liittyvät	4	5
Tiivistelmä	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.