Machine learningMachine learning
正则化 CatBoost
正则化 CatBoost 在 CatBoost 的有序梯度提升框架之上，应用了显式正则化控制——L2 叶子正则化、树深度约束、收缩率和模型大小惩罚——从而在保留 CatBoost 对分类特征的原生处理能力及其在表格数据集上低预测延迟的同时，减少了过拟合。
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来源
- 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 ↗
- Dorogush, A. V., Ershov, V., & Gulin, A. (2018). CatBoost: gradient boosting with categorical features support. arXiv preprint arXiv:1810.11363. link ↗
如何引用本页
ScholarGate. (2026, June 3). Regularized CatBoost (Categorical Boosting with Explicit Regularization). ScholarGate. https://scholargate.app/zh/machine-learning/regularized-catboost
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
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