Comparar métodos

	LightGBM en línea	Gradient Boosting	Gradient Boosting en Línea
Campo	Aprendizaje automático	Aprendizaje automático	Aprendizaje automático
Familia	Machine learning	Machine learning	Machine learning
Año de origen	2017 (LightGBM); 2000s (online boosting)	2001	2011–2015
Autor original	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Friedman, J. H.	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
Tipo	Online ensemble (incremental gradient boosting)	Ensemble (sequential boosting of decision trees)	Online ensemble (sequential boosting on streaming data)
Fuente seminal	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.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216.
Alias	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
Relacionados	5	5	6
Resumen	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.	Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost.	Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible.