השוואת שיטות

	הגברת גרדיאנט אנסמבל (Ensemble Gradient Boosting)	עץ החלטה
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2001	1984
הוגה השיטה	Friedman, J. H.	Breiman, Friedman, Olshen & Stone
סוג	Ensemble (sequential boosting of decision trees)	Recursive partitioning (if-then rules)
מקור מכונן	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.
כינויים	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
קשורות	6	5
תקציר	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.