विधियों की तुलना करें

	गॉसियन प्रक्रिया	बायेसियन गॉसियन प्रक्रिया	बेयसियन ऑप्टिमाइजेशन	रैंडम फ़ॉरेस्ट
क्षेत्र	मशीन अधिगम	मशीन अधिगम	अनुकूलन	मशीन अधिगम
परिवार	Machine learning	Machine learning	Process / pipeline	Machine learning
उद्भव वर्ष	2006 (book); roots in Kriging, 1951)	1978–2006	1975 (foundational); 2012 (ML standard)	2001
प्रवर्तक	Rasmussen, C. E. & Williams, C. K. I.	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.
प्रकार	Probabilistic non-parametric model	Probabilistic kernel model	Sequential model-based black-box optimization	Ensemble (bagging of decision trees)
मौलिक स्रोत	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9	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.
उपनाम	GP, Gaussian Process Regression, GPR, Kriging	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
संबंधित	3	3	2	4
सारांश	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.	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.