Machine learningMachine learning
在线 LightGBM
在线 LightGBM 增量式地应用了 Light Gradient-Boosting Machine 框架：模型无需一次性获取所有训练数据，而是在数据到达时以迷你批次（mini-batches）或数据块（data chunks）的形式进行更新。这使得 LightGBM 高效的基于直方图的提升（histogram-based boosting）能够在流式处理（streaming）、持续学习（continual-learning）和数据扩展（data-expansion）场景下部署，而无需从头开始重新训练。
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Method map
The neighbourhood of related methods — select a node to explore.
来源
- 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, 30. link ↗
- Bifet, A., & Gavalda, R. (2009). Adaptive Learning from Evolving Data Streams. Advances in Intelligent Data Analysis VIII. Lecture Notes in Computer Science, vol 5772. Springer. DOI: 10.1007/978-3-642-03915-7_22 ↗
如何引用本页
ScholarGate. (2026, June 3). Online / Incremental LightGBM (Light Gradient-Boosting Machine with Streaming Updates). ScholarGate. https://scholargate.app/zh/machine-learning/online-lightgbm
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- 梯度提升（Gradient Boosting）机器学习↔ compare
- LightGBM机器学习↔ compare
- 在线梯度提升机器学习↔ compare
- 在线学习机器学习↔ compare
- 在线随机森林机器学习↔ compare