方法对比
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| 正则化随机森林× | 正则化梯度提升× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2012 | 2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost) |
| 提出者≠ | Deng, H. & Runger, G. | Chen, T. & Guestrin, C. (building on Friedman, J. H.) |
| 类型≠ | Regularized ensemble (penalized feature selection in trees) | Regularized ensemble (additive tree model) |
| 开创性文献≠ | Deng, H., & Runger, G. (2012). Feature selection via regularized trees. Proceedings of the 2012 International Joint Conference on Neural Networks (IJCNN), IEEE, pp. 1–8. DOI ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. DOI ↗ |
| 别名 | RRF, Guided Regularized Random Forest, GRRF, regularized tree ensemble | penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting |
| 相关≠ | 5 | 6 |
| 摘要≠ | Regularized Random Forest (RRF), introduced by Deng and Runger in 2012, extends the standard Random Forest by adding a penalty that discourages splits on features not already used in the ensemble. This built-in regularization produces sparser, less redundant feature subsets, making the model especially valuable when feature selection is as important as predictive accuracy. | Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data. |
| ScholarGate数据集 ↗ |
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