Сравнение методов
Просматривайте выбранные методы рядом; строки с различиями подсвечены.
| Устойчивый LightGBM× | LightGBM× | |
|---|---|---|
| Область | Машинное обучение | Машинное обучение |
| Семейство | Machine learning | Machine learning |
| Год появления≠ | 2017 (LightGBM); robust variants widely adopted 2018–present | 2017 |
| Автор метода≠ | Ke, G. et al. (LightGBM); robust objectives adapted from Friedman, J. H. | Ke, G. et al. (Microsoft) |
| Тип≠ | Ensemble (gradient boosted decision trees with robust loss) | Gradient boosting decision tree ensemble |
| Основополагающий источник≠ | 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 ↗ | 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 (NeurIPS) 30, 3146–3154. link ↗ |
| Другие названия | Robust LGBM, LightGBM with Huber loss, outlier-resistant gradient boosting, robust gradient boosted trees | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Связанные≠ | 6 | 5 |
| Сводка≠ | Robust LightGBM is a gradient boosting framework that pairs Microsoft's highly efficient LightGBM engine with outlier-resistant loss functions — most commonly Huber, quantile, or mean absolute error — so that predictions are not unduly distorted by extreme or erroneous observations. It retains LightGBM's speed and leaf-wise tree growth while providing resistance to heavy-tailed noise in the target variable. | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. |
| ScholarGateНабор данных ↗ |
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