Confronta i metodi
Esamina i metodi selezionati fianco a fianco; le righe che differiscono sono evidenziate.
| CatBoost× | Gradient Boosting× | LightGBM× | |
|---|---|---|---|
| Campo | Apprendimento automatico | Apprendimento automatico | Apprendimento automatico |
| Famiglia | Machine learning | Machine learning | Machine learning |
| Anno di origine≠ | 2018 | 2001 | 2017 |
| Ideatore≠ | Prokhorenkova, L. et al. (Yandex) | Friedman, J. H. | Ke, G. et al. (Microsoft) |
| Tipo≠ | Gradient boosting on decision trees | Ensemble (sequential boosting of decision trees) | Gradient boosting decision tree ensemble |
| Fonte seminale≠ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ | 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 ↗ |
| Alias | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma | Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine | LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting |
| Correlati | 5 | 5 | 5 |
| Sintesi≠ | CatBoost is a gradient boosting algorithm, introduced by Prokhorenkova and colleagues at Yandex in 2018, that handles categorical variables natively and uses ordered target encoding to avoid label leakage. By building an additive ensemble of trees while permuting the data order at each iteration, it is often superior to XGBoost and LightGBM on category-heavy data. | 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. |
| ScholarGateInsieme di dati ↗ |
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