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| 부스팅× | 그래디언트 부스팅× | LightGBM× | |
|---|---|---|---|
| 분야 | 머신러닝 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning | Machine learning |
| 기원 연도≠ | 1990–1997 | 2001 | 2017 |
| 창시자≠ | Schapire, R. E.; Freund, Y. | Friedman, J. H. | Ke, G. et al. (Microsoft) |
| 유형≠ | Sequential ensemble (iterative reweighting) | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble |
| 원전≠ | 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 ↗ | 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 ↗ |
| 별칭 | AdaBoost, gradient boosting, iterative reweighting ensemble, sequential ensemble | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| 관련≠ | 6 | 5 | 5 |
| 요약≠ | 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. | 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. |
| ScholarGate데이터셋 ↗ |
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