Bandingkan metode

	Boosting Teregularisasi	Peningkatan Gradien
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	2001–2016	2001
Pencetus	Friedman, J. H.; extended by Chen & Guestrin	Friedman, J. H.
Tipe	Regularized ensemble (boosting with shrinkage/penalty)	Ensemble (sequential boosting of decision trees)
Sumber perintis	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.
Alias	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
Terkait	5	5
Ringkasan	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.