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| Online logistische Regression× | Logistische Regression (ML)× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1960s (perceptron); formalized for logistic loss ~2000s | 1958 |
| Urheber≠ | Rosenblatt, F. / Widrow, B. (perceptron era); modern SGD form: Bottou, L. | Cox, D. R. |
| Typ≠ | Incremental supervised classifier | Probabilistic linear classifier |
| Wegweisende Quelle≠ | Bottou, L. (2010). Large-Scale Machine Learning with Stochastic Gradient Descent. In Proceedings of COMPSTAT 2010, 177–186. Physica-Verlag. link ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Aliasnamen | incremental logistic regression, streaming logistic regression, SGD logistic classifier, online binary classifier | logit model, logit regression, binomial logistic regression, maximum entropy classifier |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | 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. | Logistic regression is a foundational probabilistic classifier that models the log-odds of a binary (or multinomial) outcome as a linear function of the predictors. Introduced by D. R. Cox in 1958, it remains one of the most widely used and interpretable classification methods in both statistics and machine learning, valued for its calibrated probability outputs and clear coefficient interpretation. |
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