Jämför metoder

	Gradient Boosting	Regressions- och utjämningssplines
Ämnesområde	Maskininlärning	Maskininlärning
Familj	Machine learning	Machine learning
Ursprungsår	2001	1996
Upphovsperson	Friedman, J. H.	Spline regression literature; P-splines by Eilers & Marx
Typ	Ensemble (sequential boosting of decision trees)	Piecewise-polynomial nonparametric regression
Ursprungskälla	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.
Alias	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	splines, cubic splines, natural splines, smoothing splines
Närliggande	5	4
Sammanfattning	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.	Regression splines model a nonlinear relationship by fitting piecewise polynomials that join smoothly at a set of points called knots. Cubic and natural splines are the most common, and smoothing splines add a roughness penalty that automatically balances fit against smoothness. Splines are the standard flexible building block for univariate nonlinear regression and the basis of generalized additive models.