방법 비교

	온라인 LightGBM	온라인 그래디언트 부스팅
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2017 (LightGBM); 2000s (online boosting)	2011–2015
창시자	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
유형	Online ensemble (incremental gradient boosting)	Online ensemble (sequential boosting on streaming data)
원전	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.	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.
별칭	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
관련	5	6
요약	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.	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.