Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Bayesian LightGBM× | XGBoost× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2017 (LightGBM); 2012 (Bayesian optimization) | 2016 |
| Autors≠ | Ke et al. (LightGBM); Snoek et al. (Bayesian optimization) | Chen, T. & Guestrin, C. |
| Tips≠ | Gradient boosting with Bayesian hyperparameter search | Ensemble (gradient-boosted decision trees) |
| Pirmavots≠ | 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. In Advances in Neural Information Processing Systems, 30, 3146–3154. link ↗ | Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794. DOI ↗ |
| Citi nosaukumi≠ | Bayesian-tuned LightGBM, LightGBM + Bayesian optimization, BayesOpt LightGBM, LightGBM with BayesOpt | XGBoost, extreme gradient boosting, scalable tree boosting |
| Saistītās | 5 | 5 |
| Kopsavilkums≠ | Bayesian LightGBM combines LightGBM — a highly efficient histogram-based gradient boosting framework — with Bayesian hyperparameter optimization. Instead of exhaustive grid search or random search, a probabilistic surrogate model guides the search for optimal hyperparameters, dramatically reducing the number of costly model evaluations needed to reach strong predictive performance. | 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. |
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