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| Bayesian LightGBM× | LightGBM× | Random Forest× | |
|---|---|---|---|
| Bidang | Pembelajaran Mesin | Pembelajaran Mesin | Pembelajaran Mesin |
| Keluarga | Machine learning | Machine learning | Machine learning |
| Tahun asal≠ | 2017 (LightGBM); 2012 (Bayesian optimization) | 2017 | 2001 |
| Pengasas≠ | Ke et al. (LightGBM); Snoek et al. (Bayesian optimization) | Ke, G. et al. (Microsoft) | Breiman, L. |
| Jenis≠ | Gradient boosting with Bayesian hyperparameter search | Gradient boosting decision tree ensemble | Ensemble (bagging of 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. 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 ↗ | Breiman, L. (2001). Random Forests. Machine Learning, 45, 5–32. DOI ↗ |
| Alias | Bayesian-tuned LightGBM, LightGBM + Bayesian optimization, BayesOpt LightGBM, LightGBM with BayesOpt | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting | Rastgele Orman (Random Forest), rastgele orman, random decision forest, bagged tree ensemble |
| Berkaitan≠ | 5 | 5 | 4 |
| Ringkasan≠ | 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. | Random Forest is an ensemble learning method, introduced by Leo Breiman in 2001, that grows many decision trees on bootstrap samples of the data and combines their votes to produce strong classification and regression. By pooling many slightly different trees, it produces more accurate and more stable predictions than any single tree. |
| ScholarGateSet data ↗ |
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