Salīdzināt metodes
Apskatiet izvēlētās metodes blakus; rindas, kas atšķiras, ir izceltas.
| Ensemble Gradient Boosting× | CatBoost× | |
|---|---|---|
| Nozare | Mašīnmācīšanās | Mašīnmācīšanās |
| Saime | Machine learning | Machine learning |
| Izcelsmes gads≠ | 2001 | 2018 |
| Autors≠ | Friedman, J. H. | Prokhorenkova, L. et al. (Yandex) |
| Tips≠ | Ensemble (sequential boosting of decision trees) | Gradient boosting on decision trees |
| Pirmavots≠ | Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. DOI ↗ | Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018. DOI ↗ |
| Citi nosaukumi | Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting | CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma |
| Saistītās≠ | 6 | 5 |
| Kopsavilkums≠ | Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data. | 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. |
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