विधियों की तुलना करें

	CatBoost	ह्यूबर रिग्रेशन	लाइटजीबीएम
क्षेत्र	मशीन अधिगम	सांख्यिकी	मशीन अधिगम
परिवार	Machine learning	Regression model	Machine learning
उद्भव वर्ष	2018	1964	2017
प्रवर्तक	Prokhorenkova, L. et al. (Yandex)	Peter J. Huber	Ke, G. et al. (Microsoft)
प्रकार	Gradient boosting on decision trees	Robust linear regression (M-estimation)	Gradient boosting decision tree ensemble
मौलिक स्रोत	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.	Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q. & Liu, T.-Y. (2017). LightGBM: A Highly Efficient Gradient Boosting Decision Tree. Advances in Neural Information Processing Systems (NeurIPS) 30, 3146–3154.
उपनाम	CatBoost (Categorical Boosting), categorical boosting, ordered boosting, kategorik gradyan artırma	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
संबंधित	5	5	5
सारांश	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.	LightGBM is Microsoft's gradient boosting decision tree implementation, introduced by Ke and colleagues in 2017, that grows trees leaf-wise and bins features into histograms for speed. On large datasets it is much faster than XGBoost while retaining strong predictive accuracy.