Bandingkan metode
Tinjau metode pilihan Anda berdampingan; baris yang berbeda akan disorot.
| LightGBM× | XGBoost× | |
|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning |
| Tahun asal≠ | 2017 | 2016 |
| Pencetus≠ | Ke, G. et al. (Microsoft) | Chen, T. & Guestrin, C. |
| Tipe≠ | Gradient boosting decision tree ensemble | Ensemble (gradient-boosted decision trees) |
| Sumber perintis≠ | 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≠ | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | XGBoost, extreme gradient boosting, scalable tree boosting |
| Terkait | 5 | 5 |
| Ringkasan≠ | 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. |
| ScholarGateSet data ↗ |
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