Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Regulariserad LightGBM× | XGBoost× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2017 | 2016 |
| Upphovsperson≠ | Ke, G. et al. (Microsoft Research) | Chen, T. & Guestrin, C. |
| Typ≠ | Regularized gradient boosting ensemble | Ensemble (gradient-boosted decision trees) |
| Ursprungskälla≠ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., & Liu, T.-Y. (2017). LightGBM: A highly efficient gradient boosting decision tree. Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM | XGBoost, extreme gradient boosting, scalable tree boosting |
| Närliggande | 5 | 5 |
| Sammanfattning≠ | Regularized LightGBM applies L1 (lasso) and L2 (ridge) penalty terms to the leaf weight objective of LightGBM — Microsoft's highly efficient gradient boosting framework — to control model complexity, reduce overfitting, and improve generalization on tabular classification and regression tasks with high-dimensional or noisy feature sets. | 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. |
| ScholarGateDatamängd ↗ |
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