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| 강건 선형 회귀× | 선형 회귀 (ML)× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1964–1987 | 1805–1809 |
| 창시자≠ | Huber, P. J.; Rousseeuw, P. J. | Legendre, A.-M. & Gauss, C.F. |
| 유형≠ | Outlier-resistant supervised regression | Supervised regression |
| 원전≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | 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 |
| 별칭 | robust regression, M-estimator regression, Huber regression, outlier-resistant regression | ordinary least squares regression, OLS, least squares regression, multiple linear regression |
| 관련 | 5 | 5 |
| 요약≠ | 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. |
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