Sammenlign metoder

	Online LightGBM	Online Random Forest
Fagområde	Maskinlæring	Maskinlæring
Familie	Machine learning	Machine learning
Oprindelsesår	2017 (LightGBM); 2000s (online boosting)	2009
Ophavsperson	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Saffari, A. et al.
Type	Online ensemble (incremental gradient boosting)	Incremental ensemble (streaming decision trees)
Oprindelig kilde	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.	Saffari, A., Leistner, C., Santner, J., Godec, M., & Bischof, H. (2009). On-line random forests. In Proceedings of the 3rd IEEE International Workshop on On-Line Learning for Computer Vision (OLCV 2009), pp. 1–8. IEEE.
Aliasser	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	ORF, streaming random forest, incremental random forest, adaptive random forest
Relaterede	5	6
Resumé	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 Random Forest (ORF) extends the classic Random Forest to streaming settings, updating each tree incrementally as new observations arrive without storing or replaying the full training set. Algorithms such as Adaptive Random Forests (ARF) add drift detection so the ensemble adapts when the data distribution changes over time.