Σύγκριση μεθόδων

	CatBoost	Παλινδρόμηση Huber
Πεδίο	Μηχανική Μάθηση	Στατιστική
Οικογένεια	Machine learning	Regression model
Έτος προέλευσης	2018	1964
Δημιουργός	Prokhorenkova, L. et al. (Yandex)	Peter J. Huber
Τύπος	Gradient boosting on decision trees	Robust linear regression (M-estimation)
Θεμελιώδης πηγή	Prokhorenkova, L., Gusev, G., Vorobev, A., Dorogush, A.V. & Gulin, A. (2018). CatBoost: Unbiased Boosting with Categorical Features. In NeurIPS 2018.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.
Εναλλακτικές ονομασίες	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
Συναφείς	5	5
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.	Huber regression is a robust linear regression method, introduced by Peter J. Huber in 1964, that resists the influence of outliers by treating small and large residuals differently. It applies a squared (OLS-like) loss to small residuals and a milder absolute-value loss to large ones, so extreme observations cannot dominate the fit.