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| Lasso-Regression× | Logistische Regression× | |
|---|---|---|
| Fachgebiet≠ | Maschinelles Lernen | Forschungsstatistik |
| Familie≠ | Machine learning | Process / pipeline |
| Entstehungsjahr≠ | 1996 | 1958 |
| Urheber≠ | Tibshirani, R. | David Roxbee Cox |
| Typ≠ | Regularized linear regression (L1 penalty) | Method |
| 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≠ | LASSO Regresyonu, lasso, L1-regularized regression, L1 regularization | logit model, binomial logistic regression, LR |
| Verwandt≠ | 4 | 3 |
| Zusammenfassung≠ | Lasso regression, introduced by Robert Tibshirani in 1996, is a linear regression method that adds an L1 penalty to the loss so that it shrinks coefficients and performs variable selection at the same time, producing a sparse model. By driving some coefficients exactly to zero it keeps only the predictors that matter. | Logistic regression is a statistical method for modeling the probability of a binary outcome (disease present/absent, success/failure) as a function of continuous and categorical predictors. Developed by David Roxbee Cox (1958), it solves the problem of predicting categorical outcomes by applying a logistic transformation to constrain predictions to the [0,1] probability interval, enabling accurate risk stratification, diagnostic prediction, and causal inference in epidemiology, medicine, and social science. |
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