Compara mètodes

	CatBoost Regularitzat	Gradient Boosting	LightGBM Regularitzat
Camp	Aprenentatge automàtic	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning	Machine learning
Any d'origen	2018	2001	2017
Autor original	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)	Friedman, J. H.	Ke, G. et al. (Microsoft Research)
Tipus	Regularized gradient boosting ensemble	Ensemble (sequential boosting of decision trees)	Regularized gradient boosting ensemble
Font seminal	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (2018). CatBoost: unbiased boosting with categorical features. Advances in Neural Information Processing Systems, 31.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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, 30, 3146–3154.
Àlies	CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM
Relacionats	5	5	5
Resum	Regularized CatBoost applies explicit regularization controls — L2 leaf regularization, tree depth constraints, shrinkage rate, and model size penalties — on top of CatBoost's ordered gradient boosting framework, reducing overfitting while retaining CatBoost's native handling of categorical features and its low prediction latency on tabular datasets.	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.	Regularized LightGBM applies L1 (lasso) and L2 (ridge) penalty terms to the leaf weight objective of LightGBM — Microsoft's highly efficient gradient boosting framework — to control model complexity, reduce overfitting, and improve generalization on tabular classification and regression tasks with high-dimensional or noisy feature sets.