Сравнение методов

	Мультивариантные адаптивные регрессионные сплайны (MARS)	Обобщенная аддитивная модель (GAM)	Градиентный бустинг
Область	Машинное обучение	Машинное обучение	Машинное обучение
Семейство	Machine learning	Machine learning	Machine learning
Год появления	1991	1986	2001
Автор метода	Jerome H. Friedman	Trevor Hastie & Robert Tibshirani	Friedman, J. H.
Тип	Adaptive piecewise-linear regression	Semi-parametric additive regression model	Ensemble (sequential boosting of decision trees)
Основополагающий источник	Friedman, J. H. (1991). Multivariate adaptive regression splines. The Annals of Statistics, 19(1), 1–67.	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.
Другие названия	multivariate adaptive regression splines, earth algorithm, MARS regression, çok değişkenli uyarlamalı regresyon spline'ları	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Связанные	4	4	5
Сводка	Multivariate adaptive regression splines, introduced by Jerome Friedman in 1991, is a flexible nonparametric regression method that automatically models nonlinearities and interactions by combining piecewise-linear 'hinge' functions. It builds the model in a forward stagewise pass that adds basis functions where they help most, then prunes back the overgrown model, yielding an interpretable additive-plus-interaction form that adapts its complexity to the data.	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.