Comparer des méthodes

	Processus Gaussien	Forêt Aléatoire
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2006 (book); roots in Kriging, 1951)	2001
Auteur d'origine	Rasmussen, C. E. & Williams, C. K. I.	Breiman, L.
Type	Probabilistic non-parametric 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, Gaussian Process Regression, GPR, Kriging	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	3	4
Résumé	A Gaussian Process (GP) is a non-parametric, fully probabilistic machine learning model that places a prior distribution directly over functions. Rather than predicting a single value, it returns a predictive mean and a calibrated uncertainty estimate at every test point, making it especially valuable for regression on small to medium datasets and for Bayesian optimization tasks.	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.