Compara mètodes
Revisa els mètodes seleccionats l'un al costat de l'altre; les files que difereixen es ressalten.
| CatBoost× | LightGBM Regularitzat× | |
|---|---|---|
| Camp | Aprenentatge automàtic | Aprenentatge automàtic |
| Família | Machine learning | Machine learning |
| Any d'origen≠ | 2018 | 2017 |
| Autor original≠ | Prokhorenkova, L. et al. (Yandex) | Ke, G. et al. (Microsoft Research) |
| Tipus≠ | Gradient boosting on decision trees | Regularized gradient boosting ensemble |
| Font seminal≠ | 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, 30, 3146–3154. link ↗ |
| Àlies | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM |
| Relacionats | 5 | 5 |
| Resum≠ | 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. | 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. |
| ScholarGateConjunt de dades ↗ |
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