Jämför metoder
Granska de valda metoderna sida vid sida; rader som skiljer sig är markerade.
| Bayesianskt XGBoost× | LightGBM× | |
|---|---|---|
| Ämnesområde | Maskininlärning | Maskininlärning |
| Familj | Machine learning | Machine learning |
| Ursprungsår≠ | 2012–2016 | 2017 |
| Upphovsperson≠ | Chen, T. & Guestrin, C. (XGBoost); Snoek, J. et al. (Bayesian Optimization) | Ke, G. et al. (Microsoft) |
| Typ≠ | Ensemble (gradient boosted trees with Bayesian hyperparameter search) | Gradient boosting decision tree ensemble |
| Ursprungskälla≠ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. 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 ↗ |
| Alias | Bayesian XGBoost, XGBoost with Bayesian Optimization, BayesOpt-XGBoost, Bayes-tuned XGBoost | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Närliggande≠ | 4 | 5 |
| Sammanfattning≠ | Bayesian XGBoost combines the predictive power of Extreme Gradient Boosting with Bayesian optimization for hyperparameter tuning. Instead of grid or random search, a probabilistic surrogate model guides the search for optimal learning rate, tree depth, and regularization parameters, achieving near-peak performance with far fewer evaluations than exhaustive search approaches. | 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. |
| ScholarGateDatamängd ↗ |
|
|