Сравнение на методи

	Ансамблово усилване на градиента (Ensemble Gradient Boosting)	CatBoost
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2001	2018
Създател	Friedman, J. H.	Prokhorenkova, L. et al. (Yandex)
Тип	Ensemble (sequential boosting of decision trees)	Gradient boosting on decision trees
Основополагащ източник	Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232.	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.
Други названия	Gradient Boosting Machine, GBM, Gradient Tree Boosting, Stochastic Gradient Boosting	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma
Свързани	6	5
Резюме	Gradient Boosting is an ensemble method introduced by Jerome Friedman in 2001 that builds a strong predictive model by sequentially adding shallow decision trees, each correcting the errors of the previous ensemble. By framing the problem as gradient descent in function space, it achieves state-of-the-art accuracy on classification, regression, and ranking tasks across tabular data.	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.