Porównaj metody

	Drzewo decyzyjne	Uogólniony model addytywny (GAM)	Regression Splines
Dziedzina	Uczenie maszynowe	Uczenie maszynowe	Uczenie maszynowe
Rodzina	Machine learning	Machine learning	Machine learning
Rok powstania	1984	1986	1996
Twórca	Breiman, Friedman, Olshen & Stone	Trevor Hastie & Robert Tibshirani	Spline regression literature; P-splines by Eilers & Marx
Typ	Recursive partitioning (if-then rules)	Semi-parametric additive regression model	Piecewise-polynomial nonparametric regression
Źródło pierwotne	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.	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.
Inne nazwy	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	GAM, additive model, spline-based additive regression, Genelleştirilmiş toplamsal model	splines, cubic splines, natural splines, smoothing splines
Pokrewne	5	4	4
Podsumowanie	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.	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.