Comparer des méthodes

	AdaBoost	LightGBM
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1997	2017
Auteur d'origine	Freund, Y. & Schapire, R.E.	Ke, G. et al. (Microsoft)
Type	Ensemble (sequential boosting of weak learners)	Gradient boosting decision tree ensemble
Source fondatrice	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.
Alias	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
Apparentées	5	5
Résumé	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.