Comparer des méthodes

	Processus Gaussien Bayésien	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1978–2006	2001
Auteur d'origine	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.	Breiman, L.
Type	Probabilistic kernel model	Ensemble (bagging of decision trees)
Source fondatrice	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
Alias	GP regression, GPR, Gaussian process model, GP classifier	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	4
Résumé	A Bayesian Gaussian Process (GP) places a probability distribution directly over functions, using a kernel to encode similarity between inputs. After observing data, Bayes' rule converts this prior into a posterior that yields not just point predictions but calibrated uncertainty estimates at every new input — making it one of the most principled probabilistic models in machine learning.	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.