Machine learningMachine learning
Regularized Logistic Regression
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.
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Sources
- Tibshirani, R. (1996). Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B, 58(1), 267–288. DOI: 10.1111/j.2517-6161.1996.tb02080.x ↗
- Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning (2nd ed., Ch. 4, 18). Springer. ISBN: 978-0-387-84857-0
Related methods
Referenced by
Elastic Net RegressionLogistic regression (ML)Online Logistic RegressionRegularized Federated LearningRegularized k-nearest neighborsRegularized linear regressionRegularized Naive BayesRegularized Online LearningRegularized semi-supervised learningRegularized Support Vector MachineRegularized Transfer Learning