Krahasoni metodat

	Procesi Gaussian	Pylli i Rastësishëm
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2006 (book); roots in Kriging, 1951)	2001
Krijuesi	Rasmussen, C. E. & Williams, C. K. I.	Breiman, L.
Lloji	Probabilistic non-parametric model	Ensemble (bagging of decision trees)
Burimi themelues	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.
Emërtime të tjera	GP, Gaussian Process Regression, GPR, Kriging	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Të lidhura	3	4
Përmbledhja	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.