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| LightGBM× | Uczenie online× | |
|---|---|---|
| Dziedzina | Uczenie maszynowe | Uczenie maszynowe |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2017 | 1958–2000s |
| Twórca≠ | Ke, G. et al. (Microsoft) | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| Typ≠ | Gradient boosting decision tree ensemble | Learning paradigm (sequential model update) |
| Źródło pierwotne≠ | 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 ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| Inne nazwy | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | incremental learning, sequential learning, streaming learning, online machine learning |
| Pokrewne≠ | 5 | 6 |
| Podsumowanie≠ | 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. |
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