השוואת שיטות

	עץ החלטה	גרדיאנט בוסטינג
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	1984	2001
הוגה השיטה	Breiman, Friedman, Olshen & Stone	Friedman, J. H.
סוג	Recursive partitioning (if-then rules)	Ensemble (sequential boosting of decision trees)
מקור מכונן	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
כינויים	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
קשורות	5	5
תקציר	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.