Vertaile menetelmiä

	Regularisoitu gradienttivahvistus	XGBoost
Tieteenala	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)	2016
Kehittäjä	Chen, T. & Guestrin, C. (building on Friedman, J. H.)	Chen, T. & Guestrin, C.
Tyyppi	Regularized ensemble (additive tree model)	Ensemble (gradient-boosted decision trees)
Alkuperäislähde	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Rinnakkaisnimet	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting	XGBoost, extreme gradient boosting, scalable tree boosting
Liittyvät	6	5
Tiivistelmä	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.	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.