Salīdzināt metodes

	Bagging Ensemble	XGBoost
Nozare	Ansambļu mācīšanās	Mašīnmācīšanās
Saime	Machine learning	Machine learning
Izcelsmes gads	1996	2016
Autors	Leo Breiman	Chen, T. & Guestrin, C.
Tips	parallel ensemble	Ensemble (gradient-boosted decision trees)
Pirmavots	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.
Citi nosaukumi	bootstrap aggregating	XGBoost, extreme gradient boosting, scalable tree boosting
Saistītās	4	5
Kopsavilkums	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.