Compară metode

	Pădurea Aleatoare (Random Forest)	Arbore de decizie	Mașina cu Vectori Suport (Clasificare)	XGBoost
Domeniu	Învățare automată	Învățare automată	Învățare automată	Învățare automată
Familie	Machine learning	Machine learning	Machine learning	Machine learning
Anul apariției	2001	1984	1995	2016
Autorul original	Breiman, L.	Breiman, Friedman, Olshen & Stone	Cortes, C. & Vapnik, V.	Chen, T. & Guestrin, C.
Tip	Ensemble (bagging of decision trees)	Recursive partitioning (if-then rules)	Maximum-margin classifier (kernel method)	Ensemble (gradient-boosted decision trees)
Sursa seminală	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.	Cortes, C. & Vapnik, V. (1995). Support-Vector Networks. Machine Learning, 20, 273–297.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Denumiri alternative	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree	Destek Vektör Makinesi (SVM — Sınıflandırma), support-vector network, SVM classifier, maximum-margin classifier	XGBoost, extreme gradient boosting, scalable tree boosting
Înrudite	4	5	5	5
Rezumat	Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree.	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.	The Support Vector Machine, introduced by Corinna Cortes and Vladimir Vapnik in 1995, is a classifier that finds the optimal separating hyperplane between classes in a high-dimensional space. It chooses the boundary that leaves the widest possible margin to the nearest training points, which makes its decisions robust on new data.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.