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集成梯度提升

梯度提升是一种集成方法，由 Jerome Friedman 于 2001 年提出，它通过依次添加浅层决策树来构建强大的预测模型，每棵树都纠正先前集成模型的错误。通过将问题建模为函数空间中的梯度下降，该方法在表格数据的分类、回归和排序任务上取得了最先进的准确性。

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集成梯度提升
AdaBoostCatBoost决策树LightGBM随机森林XGBoost

来源

  1. Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI: 10.1214/aos/1013203451 ↗
  2. Friedman, J. H. (2002). Stochastic gradient boosting. Computational Statistics and Data Analysis, 38(4), 367–378. DOI: 10.1016/S0167-9473(01)00065-2 ↗

如何引用本页

ScholarGate. (2026, June 3). Gradient Boosting Machine (Ensemble of Additive Decision Trees). ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-gradient-boosting

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ScholarGate — Ensemble Gradient Boosting (Gradient Boosting Machine (Ensemble of Additive Decision Trees)). 于 2026-06-15 检索自 https://scholargate.app/zh/machine-learning/ensemble-gradient-boosting · 数据集： https://doi.org/10.5281/zenodo.20539026