השוואת שיטות

	CatBoost	גרדיאנט בוסטינג
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2018	2001
הוגה השיטה	Prokhorenkova, L. et al. (Yandex)	Friedman, J. H.
סוג	Gradient boosting on decision trees	Ensemble (sequential boosting of decision trees)
מקור מכונן	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.
כינויים	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Gradient Boosting (GBM), GBM, gradient boosted trees, gradient boosting machine
קשורות	5	5
תקציר	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.