Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| [UNTRANSLATED: AdaBoost]× | LightGBM× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 1997 | 2017 |
| Ideatore≠ | Freund, Y. & Schapire, R.E. | Ke, G. et al. (Microsoft) |
| Tipo≠ | Ensemble (sequential boosting of weak learners) | Gradient boosting decision tree ensemble |
| Fonte seminale≠ | 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 ↗ |
| Alias≠ | AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Correlati | 5 | 5 |
| Sintesi≠ | AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification. | 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. |
| ScholarGateInsieme di dati ↗ |
|
|