পদ্ধতির তুলনা করুন

	বুস্টিং	গ্রেডিয়েন্ট বুস্টিং	লাইটজিবিএম	নিয়ন্ত্রিত সিদ্ধান্ত বৃক্ষ
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning	Machine learning	Machine learning
উদ্ভবের বছর	1990–1997	2001	2017	1984
প্রবর্তক	Schapire, R. E.; Freund, Y.	Friedman, J. H.	Ke, G. et al. (Microsoft)	Breiman, L., Friedman, J., Olshen, R., & Stone, C.
ধরন	Sequential ensemble (iterative reweighting)	Ensemble (sequential boosting of decision trees)	Gradient boosting decision tree ensemble	Supervised learning (regularized tree)
মৌলিক উৎস	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.	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.	Breiman, L., Friedman, J., Olshen, R., & Stone, C. (1984). Classification and Regression Trees. Wadsworth. ISBN: 978-0-412-04841-8
অপর নাম	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	pruned decision tree, cost-complexity pruned tree, penalized decision tree, constrained CART
সম্পর্কিত	6	5	5	6
সারসংক্ষেপ	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.	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.	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.	A regularized decision tree is a decision tree model whose complexity is intentionally limited through pruning, depth constraints, or penalty terms to prevent overfitting. Rooted in Breiman et al.'s CART framework (1984), regularization converts the greedy tree-growing procedure into a bias-variance tradeoff, yielding models that generalize better to unseen data than fully-grown trees.