Improving random forest predictions in small datasets from two-phase sampling designs
Abstract Background While random forests are one of the most successful machine learning methods, it is necessary to optimize their performance for use with datasets resulting from a two-phase sampling design with a small number of cases—a common situation in biomedical studies, which often have rar...
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| Autores principales: | , , |
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| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
BMC
2021
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| Materias: | |
| Acceso en línea: | https://doaj.org/article/59888be2e459495c93e907d674a72e1a |
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