পদ্ধতির তুলনা করুন

	নিয়ন্ত্রিত বুস্টিং	বুস্টিং
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2001–2016	1990–1997
প্রবর্তক	Friedman, J. H.; extended by Chen & Guestrin	Schapire, R. E.; Freund, Y.
ধরন	Regularized ensemble (boosting with shrinkage/penalty)	Sequential ensemble (iterative reweighting)
মৌলিক উৎস	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.
অপর নাম	shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble
সম্পর্কিত	5	6
সারসংক্ষেপ	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.