Compară metode

	Gradient Boosting de Ansamblu	Arbore de decizie
Domeniu	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning
Anul apariției	2001	1984
Autorul original	Friedman, J. H.	Breiman, Friedman, Olshen & Stone
Tip	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)
Sursa seminală	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Denumiri alternative	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Înrudite	6	5
Rezumat	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.