방법 비교

	온라인 선형 회귀	선형 회귀 (ML)
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	1960 (LMS); 1950 (RLS formalization)	1805–1809
창시자	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)	Legendre, A.-M. & Gauss, C.F.
유형	Incremental supervised regression	Supervised regression
원전	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.	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	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression	ordinary least squares regression, OLS, least squares regression, multiple linear regression
관련	6	5
요약	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.	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.