Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Gradient Boosting× | LightGBM× | XGBoost× | |
|---|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning | Machine learning |
| Ursprungsår≠ | 2001 | 2017 | 2016 |
| Upphovsperson≠ | Friedman, J. H. | Ke, G. et al. (Microsoft) | Chen, T. & Guestrin, C. |
| Typ≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble | Ensemble (gradient-boosted decision trees) |
| Ursprungskälla≠ | 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 ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Alias≠ | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | XGBoost, extreme gradient boosting, scalable tree boosting |
| Närliggande | 5 | 5 | 5 |
| Sammanfattning≠ | 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. | XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions. |
| ScholarGateDatamängd ↗ |
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