方法证据记录
Ensemble Gradient Boosting
Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.
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。
Gradient Boosting Machine (Ensemble of Additive Decision Trees)
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
- Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38(4), 367–378. · DOI 10.1016/S0167-9473(01)00065-2
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