Linganisha mbinu

	Regresi Laini (ML)	Urejeshaji Linear Uliodhibitiwa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1805–1809	1970–2005
Mwanzilishi	Legendre, A.-M. & Gauss, C.F.	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Aina	Supervised regression	Penalized linear model
Chanzo asilia	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	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Majina mbadala	ordinary least squares regression, OLS, least squares regression, multiple linear regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Zinazohusiana	5	4
Muhtasari	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.	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.