Krahasoni metodat

	LightGBM	Mësimi Online
Fusha	Mësimi i makinës	Mësimi i makinës
Familja	Machine learning	Machine learning
Viti i origjinës	2017	1958–2000s
Krijuesi	Ke, G. et al. (Microsoft)	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Lloji	Gradient boosting decision tree ensemble	Learning paradigm (sequential model update)
Burimi themelues	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.
Emërtime të tjera	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting	incremental learning, sequential learning, streaming learning, online machine learning
Të lidhura	5	6
Përmbledhja	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.