השוואת שיטות

	CatBoost	AdaBoost
תחום	למידת מכונה	למידת מכונה
משפחה	Machine learning	Machine learning
שנת המקור	2018	1997
הוגה השיטה	Prokhorenkova, L. et al. (Yandex)	Freund, Y. & Schapire, R.E.
סוג	Gradient boosting on decision trees	Ensemble (sequential boosting of weak learners)
מקור מכונן	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Freund, Y. & Schapire, R.E. (1997). A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting. Journal of Computer and System Sciences, 55(1), 119–139.
כינויים	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma
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.	AdaBoost (Adaptive Boosting) is the original boosting algorithm, introduced by Yoav Freund and Robert Schapire in 1997, that combines a sequence of simple weak learners by giving more weight to the observations they get wrong. The forerunner of gradient boosting, it is simple, interpretable, and a strong baseline for classification.