Võrdle meetodeid

	Online LightGBM	LightGBM	Võrgus gradient-boostimine
Valdkond	Masinõpe	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning	Machine learning
Tekkeaasta	2017 (LightGBM); 2000s (online boosting)	2017	2011–2015
Looja	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Ke, G. et al. (Microsoft)	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.
Tüüp	Online ensemble (incremental gradient boosting)	Gradient boosting decision tree ensemble	Online ensemble (sequential boosting on streaming data)
Algallikas	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.	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.
Rööpnimetused	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent
Seotud	5	5	6
Kokkuvõte	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.	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.