विधियों की तुलना करें

	बूस्टिंग	ग्रेडिएंट बूस्टिंग	ऑनलाइन लर्निंग
क्षेत्र	मशीन अधिगम	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning	Machine learning
उद्भव वर्ष	1990–1997	2001	1958–2000s
प्रवर्तक	Schapire, R. E.; Freund, Y.	Friedman, J. H.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
प्रकार	Sequential ensemble (iterative reweighting)	Ensemble (sequential boosting of decision trees)	Learning paradigm (sequential model update)
मौलिक स्रोत	Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
उपनाम	AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	incremental learning, sequential learning, streaming learning, online machine learning
संबंधित	6	5	6
सारांश	Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy.	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 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.