Comparar métodos

	Agregación de muestras bootstrap (Bagging)	XGBoost
Campo	Aprendizaje por conjuntos	Aprendizaje automático
Familia	Machine learning	Machine learning
Año de origen	1996	2016
Autor original	Leo Breiman	Chen, T. & Guestrin, C.
Tipo	parallel ensemble	Ensemble (gradient-boosted decision trees)
Fuente seminal	Breiman, L. (1996). Bagging predictors. Machine Learning, 24(2), 123-140.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Alias	bootstrap aggregating	XGBoost, extreme gradient boosting, scalable tree boosting
Relacionados	4	5
Resumen	Bagging, short for bootstrap aggregating, is an ensemble method that reduces variance by training multiple copies of a single learning algorithm on different random subsets of the training data. Each subset is created via bootstrap sampling—randomly drawing samples with replacement. Predictions are combined through majority voting (classification) or averaging (regression). Introduced by Leo Breiman in 1996, bagging forms the foundation for random forests and is particularly effective for reducing overfitting in high-variance models.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.