Comparar métodos
Examine os métodos selecionados lado a lado; as linhas que diferem ficam destacadas.
| LightGBM Online× | Gradient Boosting× | |
|---|---|---|
| Área | Aprendizado de máquina | Aprendizado de máquina |
| Família | Machine learning | Machine learning |
| Ano de origem≠ | 2017 (LightGBM); 2000s (online boosting) | 2001 |
| Autor original≠ | Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory) | Friedman, J. H. |
| Tipo≠ | Online ensemble (incremental gradient boosting) | Ensemble (sequential boosting of decision trees) |
| Fonte seminal≠ | 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 ↗ | Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ |
| Outros nomes | Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine |
| Relacionados | 5 | 5 |
| Resumo≠ | 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. | Gradient Boosting is an ensemble learning method, formalised by Jerome H. Friedman in 2001, that combines a sequence of weak learners — typically shallow decision trees — so that each new tree is fitted to minimise the residual errors of the trees before it. It is the core algorithm behind popular implementations such as XGBoost, LightGBM and CatBoost. |
| ScholarGateConjunto de dados ↗ |
|
|