विधियों की तुलना करें

	मजबूत रैखिक प्रतिगमन	ह्यूबर रिग्रेशन
क्षेत्र	मशीन अधिगम	सांख्यिकी
परिवार	Machine learning	Regression model
उद्भव वर्ष	1964–1987	1964
प्रवर्तक	Huber, P. J.; Rousseeuw, P. J.	Peter J. Huber
प्रकार	Outlier-resistant supervised regression	Robust linear regression (M-estimation)
मौलिक स्रोत	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73-101.
उपनाम	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	Huber M-estimator, Huber loss regression, robust regression, Huber Regresyonu
संबंधित	5	5
सारांश	Robust linear regression fits a linear model between predictors and a continuous outcome while down-weighting or discarding influential outliers, preventing the few anomalous observations that OLS is famously sensitive to from distorting the entire estimated line. Major variants include Huber regression, iteratively reweighted least squares (IRLS), RANSAC, and Theil-Sen estimation.	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.