Linganisha mbinu

	Urekebishaji wa Laini wa Mtandaoni	Urejeshaji Linear Uliodhibitiwa
Nyanja	Ujifunzaji wa Mashine	Ujifunzaji wa Mashine
Familia	Machine learning	Machine learning
Mwaka wa asili	1960 (LMS); 1950 (RLS formalization)	1970–2005
Mwanzilishi	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Hoerl & Kennard (Ridge, 1970); Tibshirani (Lasso, 1996); Zou & Hastie (Elastic Net, 2005)
Aina	Incremental supervised regression	Penalized linear model
Chanzo asilia	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288.
Majina mbadala	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	Ridge regression, Lasso regression, Elastic Net regression, penalized regression
Zinazohusiana	6	4
Muhtasari	Online Linear Regression fits a linear model one observation at a time, updating weights incrementally as each new data point arrives. Unlike batch least-squares, it never needs to store or re-process the full dataset, making it the natural choice for streaming data, very large datasets, and environments where the data-generating process can shift over time.	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.