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Comparison of the modified unbounded penalty and the LASSO to select predictive genes of response to chemotherapy in breast cancer
Covariate selection is a fundamental step when building sparse prediction models in order to avoid overfitting and to gain a better interpretation of the classifier without losing its predictive accuracy. In practice the LASSO regression of Tibshirani, which penalizes the likelihood of the model by...
| Autores principales: | , , , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
| Publicado: |
Public Library of Science
2018
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| Materias: | |
| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6166949/ https://www.ncbi.nlm.nih.gov/pubmed/30273405 http://dx.doi.org/10.1371/journal.pone.0204897 |