Usporedite metode

	Online Gradient Boosting	Mrežno učenje	Online Random Forest
Područje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning
Godina nastanka	2011–2015	1958–2000s	2009
Tvorac	Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)	Saffari, A. et al.
Vrsta	Online ensemble (sequential boosting on streaming data)	Learning paradigm (sequential model update)	Incremental ensemble (streaming decision trees)
Temeljni izvor	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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.
Drugi nazivi	OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent	incremental learning, sequential learning, streaming learning, online machine learning	ORF, streaming random forest, incremental random forest, adaptive random forest
Srodne	6	6	6
Sažetak	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.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.	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.