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| Peningkatan Cerun× | LightGBM× | Random Forest× | |
|---|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 2001 | 2017 | 2001 |
| Pengasas≠ | Friedman, J. H. | Ke, G. et al. (Microsoft) | Breiman, L. |
| Jenis≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble | Ensemble (bagging of decision trees) |
| Sumber perintis≠ | 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Berkaitan≠ | 5 | 5 | 4 |
| Ringkasan≠ | 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. | 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. |
| ScholarGateSet data ↗ |
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