Comparer des méthodes

	Arbre de décision	Splines de régression et de lissage
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1984	1996
Auteur d'origine	Breiman, Friedman, Olshen & Stone	Spline regression literature; P-splines by Eilers & Marx
Type	Recursive partitioning (if-then rules)	Piecewise-polynomial nonparametric regression
Source fondatrice	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Eilers, P. H. C., & Marx, B. D. (1996). Flexible smoothing with B-splines and penalties. Statistical Science, 11(2), 89–121.
Alias	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	splines, cubic splines, natural splines, smoothing splines
Apparentées	5	4
Résumé	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.	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.