Comparar métodos
Revisa los métodos seleccionados uno junto a otro; las filas que difieren aparecen resaltadas.
| Gradient Boosting en Línea× | Gradient Boosting× | |
|---|---|---|
| Campo | Aprendizaje automático | Aprendizaje automático |
| Familia | Machine learning | Machine learning |
| Año de origen≠ | 2011–2015 | 2001 |
| Autor original≠ | Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al. | Friedman, J. H. |
| Tipo≠ | Online ensemble (sequential boosting on streaming data) | Ensemble (sequential boosting of decision trees) |
| Fuente seminal≠ | 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. link ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Alias | OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relacionados≠ | 6 | 5 |
| Resumen≠ | 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. | 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. |
| ScholarGateConjunto de datos ↗ |
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