विधियों की तुलना करें

	ह्यूबर रिग्रेशन	लाइटजीबीएम
क्षेत्र	सांख्यिकी	मशीन अधिगम
परिवार	Regression model	Machine learning
उद्भव वर्ष	1964	2017
प्रवर्तक	Peter J. Huber	Ke, G. et al. (Microsoft)
प्रकार	Robust linear regression (M-estimation)	Gradient boosting decision tree ensemble
मौलिक स्रोत	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	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu	LightGBM, Light Gradient Boosting Machine, lgbm, leaf-wise gradient boosting
संबंधित	5	5
सारांश	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.