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| Bayesowski XGBoost× | XGBoost× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2012–2016 | 2016 |
| Twórca≠ | Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization) | Chen, T. & Guestrin, C. |
| Typ≠ | Ensemble (gradient boosted trees with Bayesian hyperparameter search) | Ensemble (gradient-boosted decision trees) |
| Źródło pierwotne≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Inne nazwy≠ | Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost | XGBoost, extreme gradient boosting, scalable tree boosting |
| Pokrewne≠ | 4 | 5 |
| Podsumowanie≠ | Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches. | 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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