Krahasoni metodat
Shqyrtoni metodat e zgjedhura krah për krah; rreshtat që ndryshojnë janë të theksuar.
| LightGBM Bajezian× | Përmbledhja me Gradient (Gradient Boosting)× | LightGBM× | |
|---|---|---|---|
| Fusha | Mësimi i makinës | Mësimi i makinës | Mësimi i makinës |
| Familja | Machine learning | Machine learning | Machine learning |
| Viti i origjinës≠ | 2017 (LightGBM); 2012 (Bayesian optimization) | 2001 | 2017 |
| Krijuesi≠ | Ke et al. (LightGBM); Snoek et al. (Bayesian optimization) | Friedman, J. H. | Ke, G. et al. (Microsoft) |
| Lloji≠ | Gradient boosting with Bayesian hyperparameter search | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble |
| Burimi themelues≠ | 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 ↗ | 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 ↗ |
| Emërtime të tjera | Bayesian-tuned LightGBM, LightGBM + Bayesian optimization, BayesOpt LightGBM, LightGBM with BayesOpt | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Të lidhura | 5 | 5 | 5 |
| Përmbledhja≠ | 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. | 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. |
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