Comparer des méthodes

	Régression logistique en ligne	Régression linéaire en ligne
Domaine	Apprentissage automatique	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	1960s (perceptron); formalized for logistic loss ~2000s	1960 (LMS); 1950 (RLS formalization)
Auteur d'origine	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)
Type	Incremental supervised classifier	Incremental supervised regression
Source fondatrice	Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag.	Shalev-Shwartz, S. (2012). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier	incremental linear regression, streaming linear regression, recursive least squares regression, stochastic gradient descent regression
Apparentées	5	6
Résumé	Online Logistic Regression fits a logistic classifier one sample (or mini-batch) at a time via stochastic gradient descent, updating model weights as each observation arrives rather than waiting to see the full dataset. This makes it the standard choice for high-volume, streaming, or memory-constrained binary classification problems where batch training is infeasible.	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.