Bandingkan metode

	Boosting	Boosting Teregularisasi
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1990–1997	2001–2016
Pencetus	Schapire, R. E.; Freund, Y.	Friedman, J. H.; extended by Chen & Guestrin
Tipe	Sequential ensemble (iterative reweighting)	Regularized ensemble (boosting with shrinkage/penalty)
Sumber perintis	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.	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.
Alias	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting
Terkait	6	5
Ringkasan	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.	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.