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| 강건 선형 회귀× | 정규화 선형 회귀× | |
|---|---|---|
| 분야 | 머신러닝 | 머신러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 1964–1987 | 1970–2005 |
| 창시자≠ | Huber, P. J.; Rousseeuw, P. J. | Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005) |
| 유형≠ | Outlier-resistant supervised regression | Penalized linear model |
| 원전≠ | Huber, P. J. (1964). Robust Estimation of a Location Parameter. Annals of Mathematical Statistics, 35(1), 73–101. DOI ↗ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ |
| 별칭 | robust regression, M-estimator regression, Huber regression, outlier-resistant regression | Ridge regression, Lasso regression, Elastic Net regression, penalized regression |
| 관련≠ | 5 | 4 |
| 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. | 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. |
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