Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| LightGBM× | Random Forest× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2017 | 2001 |
| Ideatore≠ | Ke, G. et al. (Microsoft) | Breiman, L. |
| Tipo≠ | Gradient boosting decision tree ensemble | Ensemble (bagging of decision trees) |
| Fonte seminale≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateInsieme di dati ↗ |
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