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| 정규화 부스팅× | 정규화된 경사 부스팅× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2001–2016 | 2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost) |
| 창시자≠ | Friedman, J. H.; extended by Chen & Guestrin | Chen, T. & Guestrin, C. (building on Friedman, J. H.) |
| 유형≠ | Regularized ensemble (boosting with shrinkage/penalty) | Regularized ensemble (additive tree model) |
| 원전≠ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. 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 ↗ |
| 별칭 | shrinkage boosting, penalized boosting, regularized gradient boosting, L1/L2 boosting | penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting |
| 관련≠ | 5 | 6 |
| 요약≠ | Regularized boosting extends gradient boosting by adding explicit controls — shrinkage (learning rate), L1/L2 weight penalties, subsampling, and tree-complexity limits — to the objective function and the update rule. These constraints reduce overfitting, stabilise the model on noisy or small datasets, and are the core reason why systems such as XGBoost and LightGBM consistently outperform vanilla boosting on real-world tabular benchmarks. | 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. |
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