Comparer des méthodes

	Boosting	LightGBM
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1990–1997	2017
Auteur d'origine	Schapire, R. E.; Freund, Y.	Ke, G. et al. (Microsoft)
Type	Sequential ensemble (iterative reweighting)	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, gradient boosting, iterative reweighting ensemble, sequential ensemble	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
Apparentées	6	5
Résumé	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.	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.