So sánh phương pháp

	AdaBoost	LightGBM
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1997	2017
Người khởi xướng	Freund, Y. & Schapire, R.E.	Ke, G. et al. (Microsoft)
Loại	Ensemble (sequential boosting of weak learners)	Gradient boosting decision tree ensemble
Công trình gốc	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.	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.
Tên gọi khác	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
Liên quan	5	5
Tóm tắt	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.	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.