Σύγκριση μεθόδων

	CatBoost	AdaBoost	Λογιστική Παλινδρόμηση
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Ερευνητική Στατιστική
Οικογένεια	Machine learning	Machine learning	Process / pipeline
Έτος προέλευσης	2018	1997	1958
Δημιουργός	Prokhorenkova, L. et al. (Yandex)	Freund, Y. & Schapire, R.E.	David Roxbee Cox
Τύπος	Gradient boosting on decision trees	Ensemble (sequential boosting of weak learners)	Method
Θεμελιώδης πηγή	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.	Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242.
Εναλλακτικές ονομασίες	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	AdaBoost (Adaptive Boosting), adaptive boosting, adaptif artırma	logit model, binomial logistic regression, LR
Συναφείς	5	5	3
Σύνοψη	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.	Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science.