Vertaile menetelmiä
Tarkastele valitsemiasi menetelmiä rinnakkain; eroavat rivit korostetaan.
| Verkkopohjainen LightGBM× | LightGBM× | |
|---|---|---|
| Tieteenala | Koneoppiminen | Koneoppiminen |
| Menetelmäperhe | Machine learning | Machine learning |
| Syntyvuosi≠ | 2017 (LightGBM); 2000s (online boosting) | 2017 |
| Kehittäjä≠ | Ke et al. (LightGBM); Bifet, Gavalda (online boosting theory) | Ke, G. et al. (Microsoft) |
| Tyyppi≠ | Online ensemble (incremental gradient boosting) | Gradient boosting decision tree ensemble |
| Alkuperäislähde≠ | 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 ↗ | 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 | Incremental LightGBM, LightGBM incremental training, streaming LightGBM, continual LightGBM | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Liittyvät | 5 | 5 |
| Tiivistelmä≠ | 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. | 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 ↗ |
|
|