Confronta i metodi

	Boosting	LightGBM
Campo	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning
Anno di origine	1990–1997	2017
Ideatore	Schapire, R. E.; Freund, Y.	Ke, G. et al. (Microsoft)
Tipo	Sequential ensemble (iterative reweighting)	Gradient boosting decision tree ensemble
Fonte seminale	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
Correlati	6	5
Sintesi	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.