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	CatBoost	[UNTRANSLATED: AdaBoost]	Albero decisionale
Campo	Apprendimento automatico	Apprendimento automatico	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2018	1997	1984
Ideatore	Prokhorenkova, L. et al. (Yandex)	Freund, Y. & Schapire, R.E.	Breiman, Friedman, Olshen & Stone
Tipo	Gradient boosting on decision trees	Ensemble (sequential boosting of weak learners)	Recursive partitioning (if-then rules)
Fonte seminale	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.	Breiman, L., Friedman, J.H., Olshen, R.A. & Stone, C.J. (1984). Classification and Regression Trees. Wadsworth.
Alias	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	Karar Ağacı (Decision Tree), karar ağacı, classification tree, regression tree
Correlati	5	5	5
Sintesi	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.	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.