Usporedite metode

	Regularizirani LightGBM	CatBoost	Povećanje gradijenta	Regularizirani gradijentni boosting
Područje	Strojno učenje	Strojno učenje	Strojno učenje	Strojno učenje
Obitelj	Machine learning	Machine learning	Machine learning	Machine learning
Godina nastanka	2017	2018	2001	2001 (gradient boosting); 2016 (explicit L1/L2 regularization in XGBoost)
Tvorac	Ke, G. et al. (Microsoft Research)	Prokhorenkova, L. et al. (Yandex)	Friedman, J. H.	Chen, T. & Guestrin, C. (building on Friedman, J. H.)
Vrsta	Regularized gradient boosting ensemble	Gradient boosting on decision trees	Ensemble (sequential boosting of decision trees)	Regularized ensemble (additive tree model)
Temeljni izvor	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.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Friedman, J. H. (2001). Greedy Function Approximation: A Gradient Boosting Machine. Annals of Statistics, 29(5), 1189–1232.	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.
Drugi nazivi	LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine	penalized gradient boosting, shrinkage-regularized boosting, XGBoost-style regularization, L1/L2 gradient boosting
Srodne	5	5	5	6
Sažetak	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.	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.	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.