Порівняння методів

	Регуляризований CatBoost	CatBoost
Галузь	Машинне навчання	Машинне навчання
Родина	Machine learning	Machine learning
Рік появи	2018	2018
Автор методу	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A. V., & Gulin, A. (Yandex Research)	Prokhorenkova, L. et al. (Yandex)
Тип	Regularized gradient boosting ensemble	Gradient boosting on decision trees
Основоположне джерело	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.
Інші назви	CatBoost with regularization, regularized categorical boosting, CatBoost L2 regularization, penalized CatBoost	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma
Пов'язані	5	5
Підсумок	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.