An epic dance. Regularization techniques in multiple regression.

Authors

  • Manuel Molina Gastroenterology Service Hospital Infantil Universitario La Paz, Madrid.

DOI:

https://doi.org/10.30445/rear.v16i5.1252

Keywords:

multiple regression, ridge regression, lasso regression, regularization, shrinkage

Abstract

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.

Author Biography

Manuel Molina, Gastroenterology Service Hospital Infantil Universitario La Paz, Madrid.

Especialista en Pediatrí­a y sus áreas Especí­ficas desde 1991. Actualmente ejerzo en el Servicio de Gastroenterologí­a del Hospital Infantil Universitario La Paz, de Madrid.
Además, pertenezco al Grupo de Trabajo de Pediatrí­a Basada en la Evidencia, grupo compartido entre la Asociación Española de Pediatrí­a y la Asociación Española de Pediatrí­a de Atención Primaria.

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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Published

2024-06-07

How to Cite

Molina, M. (2024). An epic dance. Regularization techniques in multiple regression. Revista Electrónica AnestesiaR, 16(5). https://doi.org/10.30445/rear.v16i5.1252

Issue

Vol. 16 No. 5 (2024): Revista Electrónica de AnestesiaR

Section

Original

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Copyright (c) 2024 Revista Electrónica AnestesiaR

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