قارن الطرق
راجع الطرق التي اخترتها جنبًا إلى جنب؛ الصفوف المختلفة مميَّزة.
| تطبيق LightGBM المُنتظم (Regularized LightGBM)× | CatBoost× | |
|---|---|---|
| المجال | تعلم الآلة | تعلم الآلة |
| العائلة | Machine learning | Machine learning |
| سنة النشأة≠ | 2017 | 2018 |
| صاحب الطريقة≠ | Ke, G. et al. (Microsoft Research) | Prokhorenkova, L. et al. (Yandex) |
| النوع≠ | Regularized gradient boosting ensemble | Gradient boosting on decision trees |
| المصدر التأسيسي≠ | 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 ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ |
| الأسماء البديلة | LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma |
| ذات صلة | 5 | 5 |
| الملخص≠ | 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. | 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. |
| ScholarGateمجموعة البيانات ↗ |
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