An epic dance. Regularization techniques in multiple regression.
DOI:
https://doi.org/10.30445/rear.v16i5.1252Keywords:
multiple regression, ridge regression, lasso regression, regularization, shrinkageAbstract
Multiple regression regularization (shrinkage) techniques can be very useful to address collinearity or overfitting problems. In addition, they can be used to select the independent variables and reduce multidimensionality, achieving more robust and easy-to-interpret models. Ridge, lasso and elastic network regression techniques are described.
References
- Linear and logistic regression. En: Zumel N, Mount J, eds. Practical Data Science with R, 2ª ed. Manning Publications Co. Shelter Island, NY, 2020;215-73.
- Hastie T, Tibshirani R, Friedman J. The Elements of Statistical Learning, 2nd ed. Springer, 2009.
- Tibshirani, R. Regression shrinkage and selection via the lasso. J R Stat Soc B Methodol. 1996; 58: 267-88.
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