方法对比

	CatBoost	Huber回归
领域	机器学习	统计学
方法族	Machine learning	Regression model
起源年份	2018	1964
提出者	Prokhorenkova, L. et al. (Yandex)	Peter J. Huber
类型	Gradient boosting on decision trees	Robust linear regression (M-estimation)
开创性文献	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.
别名	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
相关	5	5
摘要	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.