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Trabajo Fin de Grado
Sparse Methods in Classification and Regression
dc.contributor.advisor | Carrizosa Priego, Emilio José | es |
dc.creator | Rivero Martínez, Antonio | es |
dc.date.accessioned | 2022-06-22T10:29:06Z | |
dc.date.available | 2022-06-22T10:29:06Z | |
dc.date.issued | 2021-06 | |
dc.identifier.citation | Rivero Martínez, A. (2021). Sparse Methods in Classification and Regression. (Trabajo Fin de Grado Inédito). Universidad de Sevilla, Sevilla. | |
dc.identifier.uri | https://hdl.handle.net/11441/134595 | |
dc.description.abstract | 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. | es |
dc.format | application/pdf | es |
dc.format.extent | 59 p. | es |
dc.language.iso | eng | es |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.title | Sparse Methods in Classification and Regression | es |
dc.type | info:eu-repo/semantics/bachelorThesis | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Estadística e Investigación Operativa | es |
dc.description.degree | Universidad de Sevilla. Grado en Matemáticas | es |
dc.publication.endPage | 59 | es |
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GM RIVERO MARTÍNEZ, ANTONIO.pdf | 1.811Mb | [PDF] | Ver/ | |