পদ্ধতির তুলনা করুন

	রোবাস্ট লিনিয়ার রিগ্রেশন	রৈখিক নির্ভরণ (ML)
ক্ষেত্র	যন্ত্র শিখন	যন্ত্র শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	1964–1987	1805–1809
প্রবর্তক	Huber, P. J.; Rousseeuw, P. J.	Legendre, A.-M. & Gauss, C.F.
ধরন	Outlier-resistant supervised regression	Supervised regression
মৌলিক উৎস	Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101.	Hastie, T., Tibshirani, R. & Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction (2nd ed., Ch. 3). Springer. ISBN: 978-0-387-84858-7
অপর নাম	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	ordinary least squares regression, OLS, least squares regression, multiple linear regression
সম্পর্কিত	5	5
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.	Linear regression fits a straight-line relationship between one or more input features and a continuous numeric outcome by minimising the sum of squared prediction errors. As a machine-learning model it is trained on labeled examples and evaluated on held-out data, making it the simplest supervised learning baseline for any regression task.