Сравнение на методи

	CatBoost	Дърво на решенията
Област	Машинно обучение	Машинно обучение
Семейство	Machine learning	Machine learning
Година на възникване	2018	1984
Създател	Prokhorenkova, L. et al. (Yandex)	Breiman, Friedman, Olshen & Stone
Тип	Gradient boosting on decision trees	Recursive partitioning (if-then rules)
Основополагащ източник	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Други названия	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Свързани	5	5
Резюме	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.	A Decision Tree is an interpretable classification and regression method, formalised by Breiman, Friedman, Olshen and Stone in their 1984 CART framework, that partitions the data with hierarchical if-then rules. Each split sends observations down one branch or another until a prediction is read off the leaf.