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| Gradient Boosting en Línia× | XGBoost× | |
|---|---|---|
| Camp | Aprenentatge automàtic | Aprenentatge automàtic |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2011–2015 | 2016 |
| Autor original≠ | Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al. | Chen, T. & Guestrin, C. |
| Tipus≠ | Online ensemble (sequential boosting on streaming data) | Ensemble (gradient-boosted decision trees) |
| Font 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 ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Àlies≠ | OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent | XGBoost, extreme gradient boosting, scalable tree boosting |
| Relacionats≠ | 6 | 5 |
| Resum≠ | 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. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
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