Bandingkan kaedah

	LightGBM Dalam Talian	LightGBM	Pembelajaran Dalam Talian
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2017 (LightGBM); 2000s (online boosting)	2017	1958–2000s
Pengasas	Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory)	Ke, G. et al. (Microsoft)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Jenis	Online ensemble (incremental gradient boosting)	Gradient boosting decision tree ensemble	Learning paradigm (sequential model update)
Sumber perintis	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.	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	incremental learning, sequential learning, streaming learning, online machine learning
Berkaitan	5	5	6
Ringkasan	Online LightGBM applies the Light Gradient-Boosting Machine framework incrementally: instead of requiring all training data at once, the model is updated in mini-batches or data chunks as they arrive. This allows LightGBM's efficient histogram-based boosting to be deployed in streaming, continual-learning, and data-expansion scenarios without retraining from scratch.	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 learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.