เปรียบเทียบวิธี

	CatBoost	การถดถอยแบบฮิวเบอร์ (Huber Regression)	ไลท์จีบีเอ็ม
สาขาวิชา	การเรียนรู้ของเครื่อง	สถิติศาสตร์	การเรียนรู้ของเครื่อง
ตระกูล	Machine learning	Regression model	Machine learning
ปีกำเนิด	2018	1964	2017
ผู้ริเริ่ม	Prokhorenkova, L. et al. (Yandex)	Peter J. Huber	Ke, G. et al. (Microsoft)
ประเภท	Gradient boosting on decision trees	Robust linear regression (M-estimation)	Gradient boosting decision tree ensemble
แหล่งต้นตำรับ	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.
ชื่อเรียกอื่น	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
ที่เกี่ยวข้อง	5	5	5
สรุป	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.