Vertaile menetelmiä
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| Regularisoitu gradienttivahvistus× | Boosting× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost) | 1990–1997 |
| Kehittäjä≠ | Chen, T. & Guestrin, C. (building on Friedman, J. H.) | Schapire, R. E.; Freund, Y. |
| Tyyppi≠ | Regularized ensemble (additive tree model) | Sequential ensemble (iterative reweighting) |
| 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 ↗ | 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 ↗ |
| Rinnakkaisnimet | penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble |
| Liittyvät | 6 | 6 |
| 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. | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. |
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