手法を比較

	ベイジアン・ガウス過程	ベイズ最適化	ランダムフォレスト
分野	機械学習	最適化	機械学習
系統	Machine learning	Process / pipeline	Machine learning
提唱年	1978–2006	1975 (foundational); 2012 (ML standard)	2001
提唱者	O'Hagan, A.; Neal, R. M.; Rasmussen, C. E. & Williams, C. K. I.	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)	Breiman, L.
種類	Probabilistic kernel model	Sequential model-based black-box optimization	Ensemble (bagging of decision trees)
原典	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.
別名	GP regression, GPR, Gaussian process model, GP classifier	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
関連	3	2	4
概要	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.	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.