Сравнение на методи
Прегледайте избраните методи един до друг; редовете с разлики са откроени.
| CatBoost× | LightGBM× | |
|---|---|---|
| Област | Машинно обучение | Машинно обучение |
| Семейство | Machine learning | Machine learning |
| Година на възникване≠ | 2018 | 2017 |
| Създател≠ | Prokhorenkova, L. et al. (Yandex) | Ke, G. et al. (Microsoft) |
| Тип≠ | Gradient boosting on decision trees | Gradient boosting decision tree ensemble |
| Основополагащ източник≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | 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 ↗ |
| Други названия | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Свързани | 5 | 5 |
| Резюме≠ | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | 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 от данни ↗ |
|
|