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| Regularisierte Logistische Regression× | Logistische Regression (ML)× | |
|---|---|---|
| Fachgebiet | Maschinelles Lernen | Maschinelles Lernen |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 1996–2005 | 1958 |
| Urheber≠ | Tibshirani, R. (lasso); Hoerl & Kennard (ridge); Zou & Hastie (elastic net) | Cox, D. R. |
| Typ≠ | Penalized classification model | Probabilistic linear classifier |
| Wegweisende Quelle≠ | Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI ↗ | Cox, D. R. (1958). The regression analysis of binary sequences. Journal of the Royal Statistical Society, Series B, 20(2), 215–242. DOI ↗ |
| Aliasnamen | penalized logistic regression, L1 logistic regression, L2 logistic regression, elastic net logistic regression | logit model, logit regression, binomial logistic regression, maximum entropy classifier |
| Verwandt | 5 | 5 |
| Zusammenfassung≠ | Regularized logistic regression extends standard logistic regression by adding an L1 (lasso), L2 (ridge), or elastic net penalty to the log-likelihood, shrinking coefficients toward zero and preventing overfitting. It is the default choice for binary or multinomial classification when you want interpretable, sparse, or stable coefficient estimates in high-dimensional or collinear feature spaces. | 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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