方法证据记录
Regularized Boosting
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.
源记录
引文逐字复制自方法源记录。这些引文不代表任何层级的验证。
Regularized Gradient Boosting (Shrinkage and Penalized Objective Boosting)
分类方法记录 · ml-model / machine-learning
- Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. · DOI 10.1214/aos/1013203451
- 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 10.1145/2939672.2939785
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