Compara mètodes

	Processos Gaussianos	Random Forest
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2006 (book); roots in Kriging, 1951)	2001
Autor original	Rasmussen, C. E. & Williams, C. K. I.	Breiman, L.
Tipus	Probabilistic non-parametric model	Ensemble (bagging of decision trees)
Font seminal	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.
Àlies	GP, Gaussian Process Regression, GPR, Kriging	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Relacionats	3	4
Resum	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.