Σύγκριση μεθόδων

	Εισαγωγή στην Ανθεκτική Γραμμική Παλινδρόμηση	Γραμμική Παλινδρόμηση (ML)
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1964–1987	1805–1809
Δημιουργός	Huber, P. J.; Rousseeuw, P. J.	Legendre, A.-M. & Gauss, C.F.
Τύπος	Outlier-resistant supervised regression	Supervised regression
Θεμελιώδης πηγή	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
Εναλλακτικές ονομασίες	robust regression, M-estimator regression, Huber regression, outlier-resistant regression	ordinary least squares regression, OLS, least squares regression, multiple linear regression
Συναφείς	5	5
Σύνοψη	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.