পদ্ধতির তুলনা করুন

	রোবাস্ট লিনিয়ার রিগ্রেশন	নিয়ন্ত্রিত রৈখিক নির্ভরণ (Regularized Linear Regression)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1964–1987	1970–2005
প্রবর্তক	Huber, P. J.; Rousseeuw, P. J.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
ধরন	Outlier-resistant supervised regression	Penalized linear model
মৌলিক উৎস	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
অপর নাম	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
সম্পর্কিত	5	4
সারসংক্ষেপ	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.	Regularized linear regression adds a penalty term to the ordinary least-squares objective, shrinking or zeroing out coefficients to reduce overfitting and handle multicollinearity. The three main variants — Ridge (L2 penalty), Lasso (L1 penalty), and Elastic Net (combined L1+L2) — make linear regression usable even when features outnumber observations or predictors are highly correlated.