Sammenlign metoder
Gjennomgå de valgte metodene side om side; rader som avviker, er uthevet.
| Online LightGBM× | Gradient Boosting× | LightGBM× | |
|---|---|---|---|
| Fagfelt | Maskinlæring | Maskinlæring | Maskinlæring |
| Familie | Machine learning | Machine learning | Machine learning |
| Opprinnelsesår≠ | 2017 (LightGBM); 2000s (online boosting) | 2001 | 2017 |
| Opphavsperson≠ | Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory) | Friedman, J. H. | Ke, G. et al. (Microsoft) |
| Type≠ | Online ensemble (incremental gradient boosting) | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble |
| Opprinnelig kilde≠ | 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 ↗ | 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 ↗ |
| Alias | Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Relaterte | 5 | 5 | 5 |
| Sammendrag≠ | 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. | 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. |
| ScholarGateDatasett ↗ |
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