Vertaile menetelmiä
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| Boosting× | LightGBM× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 1990–1997 | 2017 |
| Kehittäjä≠ | Schapire, R. E.; Freund, Y. | Ke, G. et al. (Microsoft) |
| Tyyppi≠ | Sequential ensemble (iterative reweighting) | Gradient boosting decision tree ensemble |
| Alkuperäislähde≠ | Freund, Y. & Schapire, R. E. (1997). A decision-theoretic generalization of on-line learning and an application to boosting. Journal of Computer and System Sciences, 55(1), 119–139. 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 ↗ |
| Rinnakkaisnimet | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Liittyvät≠ | 6 | 5 |
| Tiivistelmä≠ | Boosting is a sequential ensemble technique that converts many simple, barely-better-than-chance learners into a single highly accurate model by repeatedly focusing training on the examples that previous learners got wrong, then combining all learners with weights proportional to their individual accuracy. | 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. |
| ScholarGateAineisto ↗ |
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