Trabajo Fin de Grado
Sparse Methods in Classification and Regression
Autor/es | Rivero Martínez, Antonio |
Director | Carrizosa Priego, Emilio José |
Departamento | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa |
Fecha de publicación | 2021-06 |
Fecha de depósito | 2022-06-22 |
Titulación | Universidad de Sevilla. Grado en Matemáticas |
Resumen | The regression problem with a large number of variables appears in various fields
of science, sparse methods make this problem more interpretable and more precise.
In this work we present the method Elastic Net, which ... The regression problem with a large number of variables appears in various fields of science, sparse methods make this problem more interpretable and more precise. In this work we present the method Elastic Net, which outperforms the Lasso in some situations. The elastic net have the grouping effect, while lasso does not, this is that strongly correlated predictors tend to "behave" in the same way. The lasso does not work well when the number of predictors is much grater than the number of observations, p>n. However, elastic net is useful in this situation. |
Cita | Rivero Martínez, A. (2021). Sparse Methods in Classification and Regression. (Trabajo Fin de Grado Inédito). Universidad de Sevilla, Sevilla. |
Ficheros | Tamaño | Formato | Ver | Descripción |
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GM RIVERO MARTÍNEZ, ANTONIO.pdf | 1.811Mb | [PDF] | Ver/ | |