Võrdle meetodeid

	CatBoost	Reguleeritud gradienttugevdus
Valdkond	Masinõpe	Masinõpe
Perekond	Machine learning	Machine learning
Tekkeaasta	2018	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Looja	Prokhorenkova, L. et al. (Yandex)	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Tüüp	Gradient boosting on decision trees	Regularized ensemble (additive tree model)
Algallikas	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Chen, T. & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794.
Rööpnimetused	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Seotud	5	6
Kokkuvõte	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.	Regularized gradient boosting extends the classic additive tree ensemble (Friedman 2001) by embedding L1 and L2 penalty terms directly into the training objective, along with a complexity penalty on tree size. Popularized by XGBoost (Chen & Guestrin 2016), this framework reduces overfitting and improves generalization compared to unpenalized boosting, while retaining the method's characteristic accuracy on tabular data.