Comparer des méthodes

	LightGBM en ligne	LightGBM
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017 (LightGBM); 2000s (online boosting)	2017
Auteur d'origine	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Ke, G. et al. (Microsoft)
Type	Online ensemble (incremental gradient boosting)	Gradient boosting decision tree ensemble
Source fondatrice	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, 30.	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	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
Apparentées	5	5
Résumé	Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-expansion scenarios without retraining from scratch.	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.