Compara mètodes

	Bagging (Bootstrap Aggregating)	Mitjana de models bayesians	Boosting	Processos Gaussianos
Camp	Aprenentatge automàtic	Bayesià	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Bayesian methods	Machine learning	Machine learning
Any d'origen	1996	1999	1990–1997	2006 (book); roots in Kriging, 1951)
Autor original	Breiman, L.	Hoeting, Madigan, Raftery & Volinsky	Schapire, R. E.; Freund, Y.	Rasmussen, C. E. & Williams, C. K. I.
Tipus	Ensemble meta-algorithm (variance reduction via bootstrap aggregation)	Bayesian model averaging	Sequential ensemble (iterative reweighting)	Probabilistic non-parametric model
Font 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.	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Rasmussen, C. E., & Williams, C. K. I. (2006). Gaussian Processes for Machine Learning. MIT Press. ISBN: 978-0-262-18253-9
Àlies	Bootstrap Aggregating, bootstrap aggregation, bagged ensemble, bagged predictor	BMA, Bayesian model combination, Bayesian Model Ortalaması (BMA)	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	GP, Gaussian Process Regression, GPR, Kriging
Relacionats	5	5	6	3
Resum	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.	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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.