مقایسهٔ روش‌ها

	CatBoost منظم‌شده	CatBoost	لایت‌جی‌بی‌ام منظم‌شده (Regularized LightGBM)
حوزه	یادگیری ماشین	یادگیری ماشین	یادگیری ماشین
خانواده	Machine learning	Machine learning	Machine learning
سال پیدایش	2018	2018	2017
پدیدآور	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)	Prokhorenkova, L. et al. (Yandex)	Ke, G. et al. (Microsoft Research)
نوع	Regularized gradient boosting ensemble	Gradient boosting on decision trees	Regularized gradient boosting ensemble
منبع بنیادین	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.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	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.
نام‌های دیگر	CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	LightGBM with L1/L2 regularization, penalized LightGBM, LightGBM ridge/lasso, regularized LGBM
مرتبط	5	5	5
خلاصه	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.	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 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.