Compară metode

	Arbore de decizie	Spline-uri de regresie și de netezire
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	1984	1996
Autorul original	Breiman, Friedman, Olshen & Stone	Spline regression literature; P-splines by Eilers & Marx
Tip	Recursive partitioning (if-then rules)	Piecewise-polynomial nonparametric regression
Sursa seminală	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.
Denumiri alternative	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	splines, cubic splines, natural splines, smoothing splines
Înrudite	5	4
Rezumat	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.