Vertaile menetelmiä
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| Regularisoitu gradienttivahvistus× | Gradient Boosting× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost) | 2001 |
| Kehittäjä≠ | Chen, T. & Guestrin, C. (building on Friedman, J. H.) | Friedman, J. H. |
| Tyyppi≠ | Regularized ensemble (additive tree model) | Ensemble (sequential boosting of decision trees) |
| Alkuperäislähde≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Rinnakkaisnimet | penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data. | 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. |
| ScholarGateAineisto ↗ |
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