Bandingkan kaedah
Semak kaedah pilihan anda secara bersebelahan; baris yang berbeza akan diserlahkan.
| Peningkatan Cerun× | LightGBM× | Peningkatkan Cerun Dalam Talian× | |
|---|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 2001 | 2017 | 2011–2015 |
| Pengasas≠ | Friedman, J. H. | Ke, G. et al. (Microsoft) | Grubb, A. & Bagnell, J. A.; Beygelzimer, A. et al. |
| Jenis≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble | Online ensemble (sequential boosting on streaming data) |
| 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 ↗ | Grubb, A. & Bagnell, J. A. (2011). Generalized Boosting Algorithms for Convex Optimization. Proceedings of the 28th International Conference on Machine Learning (ICML 2011), 1209–1216. link ↗ |
| Alias | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | OGB, streaming gradient boosting, incremental gradient boosting, online boosting with gradient descent |
| Berkaitan≠ | 5 | 5 | 6 |
| 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. | Online Gradient Boosting adapts the gradient boosting framework for streaming settings where data arrives one sample at a time rather than as a fixed batch. At each step the model computes a pseudo-residual for the incoming observation and updates a weak learner in place, growing an additive ensemble without storing or revisiting past data. This makes it suitable for real-time prediction and large-scale streaming pipelines where retraining from scratch is infeasible. |
| ScholarGateSet data ↗ |
|
|
|