เปรียบเทียบวิธี

	Regularized Boosting	Boosting
สาขาวิชา	การเรียนรู้ของเครื่อง	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Machine learning
ปีกำเนิด	2001–2016	1990–1997
ผู้ริเริ่ม	Friedman, J. H.; extended by Chen & Guestrin	Schapire, R. E.; Freund, Y.
ประเภท	Regularized ensemble (boosting with shrinkage/penalty)	Sequential ensemble (iterative reweighting)
แหล่งต้นตำรับ	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	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.
ชื่อเรียกอื่น	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
ที่เกี่ยวข้อง	5	6
สรุป	Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks.	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.