방법 비교

	가우시안 프로세스	베이지안 최적화	랜덤 포레스트
분야	머신러닝	최적화	머신러닝
계열	Machine learning	Process / pipeline	Machine learning
기원 연도	2006 (book); roots in Kriging, 1951)	1975 (foundational); 2012 (ML standard)	2001
창시자	Rasmussen, C. E. & Williams, C. K. I.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)	Breiman, L.
유형	Probabilistic non-parametric model	Sequential model-based black-box optimization	Ensemble (bagging of decision trees)
원전	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	Snoek, J., Larochelle, H., & Adams, R.P. (2012). Practical Bayesian Optimization of Machine Learning Algorithms. Advances in Neural Information Processing Systems (NeurIPS), 25.	Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32.
별칭	GP, Gaussian Process Regression, GPR, Kriging	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
관련	3	2	4
요약	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.	Bayesian Optimization is a sequential, model-based strategy for finding the optimum of expensive black-box functions with as few evaluations as possible. Rooted in the work of Mockus (1975) and brought to mainstream machine-learning practice by Snoek, Larochelle, and Adams (2012), it fits a probabilistic surrogate model — typically a Gaussian Process — to past observations and uses an acquisition function to decide where to probe next, balancing exploration of unknown regions with exploitation of promising ones.	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.