方法对比
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| LightGBM× | 在线梯度提升× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2011–2015 |
| 提出者≠ | Ke, G. et al. (Microsoft) | Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al. |
| 类型≠ | Gradient boosting decision tree ensemble | Online ensemble (sequential boosting on streaming data) |
| 开创性文献≠ | Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154. link ↗ | Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216. link ↗ |
| 别名 | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent |
| 相关≠ | 5 | 6 |
| 摘要≠ | LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy. | Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible. |
| ScholarGate数据集 ↗ |
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