Comparar métodos

	Regressão Logística Online	Regressão Linear Online
Área	Aprendizado de máquina	Aprendizado de máquina
Família	Machine learning	Machine learning
Ano de origem	1960s (perceptron); formalized for logistic loss ~2000s	1960 (LMS); 1950 (RLS formalization)
Autor original	Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L.	Widrow, B. & Hoff, M. E. (LMS); Gauss / Plackett (RLS)
Tipo	Incremental supervised classifier	Incremental supervised regression
Fonte seminal	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.
Outros nomes	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
Relacionados	5	6
Resumo	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.