Comparar métodos

	Agregación por Bootstrap (Bagging)	Promedio de Modelos Bayesianos	Proceso gaussiano
Campo	Aprendizaje automático	Bayesiano	Aprendizaje automático
Familia	Machine learning	Bayesian methods	Machine learning
Año de origen	1996	1999	2006 (book); roots in Kriging, 1951)
Autor original	Breiman, L.	Hoeting, Madigan, Raftery & Volinsky	Rasmussen, C. E. & Williams, C. K. I.
Tipo	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Bayesian model averaging	Probabilistic non-parametric model
Fuente seminal	Breiman, L. (1996). Bagging Predictors. Machine Learning, 24(2), 123–140.	Hoeting, J. A., Madigan, D., Raftery, A. E. & Volinsky, C. T. (1999). Bayesian Model Averaging: A Tutorial. Statistical Science, 14(4), 382–401.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Alias	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	GP, Gaussian Process Regression, GPR, Kriging
Relacionados	5	5	3
Resumen	Bagging, short for Bootstrap Aggregating, is an ensemble meta-algorithm introduced by Leo Breiman in 1996 that trains multiple copies of a base learner on independently drawn bootstrap samples of the training data and combines their predictions — by averaging for regression or majority vote for classification — to produce a final predictor with substantially lower variance than any single base learner.	Bayesian Model Averaging (BMA), formalised as a tutorial by Hoeting, Madigan, Raftery and Volinsky in 1999, addresses model uncertainty by averaging over all plausible model specifications rather than selecting a single best model. Each candidate model receives a posterior probability that reflects how well it fits the data given a prior, and predictions or coefficient estimates are formed as weighted averages across the entire model space. This approach reduces the bias and overconfidence that arise when a single selected model is treated as the true one.	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.