Comparer des méthodes

	Optimisation bayésienne	Forêt Aléatoire
Domaine	Optimisation	Apprentissage automatique
Famille	Process / pipeline	Machine learning
Année d'origine	1975 (foundational); 2012 (ML standard)	2001
Auteur d'origine	Mockus (1975); popularised for ML by Snoek, Larochelle & Adams (2012)	Breiman, L.
Type	Sequential model-based black-box optimization	Ensemble (bagging of decision trees)
Source fondatrice	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.
Alias	Bayesçi Optimizasyon (Hyperparameter Tuning), surrogate-based optimization, sequential model-based optimization, SMBO	Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble
Apparentées	2	4
Résumé	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.