Sammenlign metoder
Gennemgå dine valgte metoder side om side; rækker, der afviger, er fremhævet.
| Regulariseret CatBoost× | CatBoost× | |
|---|---|---|
| Fagområde | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning |
| Oprindelsesår | 2018 | 2018 |
| Ophavsperson≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research) | Prokhorenkova, L. et al. (Yandex) |
| Type≠ | Regularized gradient boosting ensemble | Gradient boosting on decision trees |
| Oprindelig kilde≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31. link ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ |
| Aliasser | CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma |
| Relaterede | 5 | 5 |
| Resumé≠ | Regularized CatBoost applies explicit regularization controls — L2 leaf regularization, tree depth constraints, shrinkage rate, and model size penalties — on top of CatBoost's ordered gradient boosting framework, reducing overfitting while retaining CatBoost's native handling of categorical features and its low prediction latency on tabular datasets. | 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. |
| ScholarGateDatasæt ↗ |
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