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| LightGBM Bayesiano× | LightGBM× | |
|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning |
| Anno di origine≠ | 2017 (LightGBM); 2012 (Bayesian optimization) | 2017 |
| Ideatore≠ | Ke et al. (LightGBM); Snoek et al. (Bayesian optimization) | Ke, G. et al. (Microsoft) |
| Tipo≠ | Gradient boosting with Bayesian hyperparameter search | Gradient boosting decision tree ensemble |
| Fonte seminale≠ | 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 ↗ | 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-tuned LightGBM, LightGBM + Bayesian optimization, BayesOpt LightGBM, LightGBM with BayesOpt | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Correlati | 5 | 5 |
| Sintesi≠ | 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. | 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. |
| ScholarGateInsieme di dati ↗ |
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