Compară metode
Examinează metodele selectate una lângă alta; rândurile care diferă sunt evidențiate.
| AdaBoost× | XGBoost× | |
|---|---|---|
| Domeniu | Învățare automată | Învățare automată |
| Familie | Machine learning | Machine learning |
| Anul apariției≠ | 1997 | 2016 |
| Autorul original≠ | Freund, Y. & Schapire, R.E. | Chen, T. & Guestrin, C. |
| Tip≠ | Ensemble (sequential boosting of weak learners) | Ensemble (gradient-boosted decision trees) |
| Sursa seminală≠ | 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. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Denumiri alternative | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | XGBoost, extreme gradient boosting, scalable tree boosting |
| Înrudite | 5 | 5 |
| Rezumat≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | 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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