Vertaile menetelmiä
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| Regularized LightGBM× | Gradient Boosting× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2017 | 2001 |
| Kehittäjä≠ | Ke, G. et al. (Microsoft Research) | Friedman, J. H. |
| Tyyppi≠ | Regularized gradient boosting ensemble | Ensemble (sequential boosting of decision trees) |
| Alkuperäislähde≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Rinnakkaisnimet | LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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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