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Improving random forest predictions in small datasets from two-phase sampling designs
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 rare outcom...
| Autores principales: | , , |
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| Formato: | Online Artículo Texto |
| Lenguaje: | English |
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BioMed Central
2021
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| Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8607560/ https://www.ncbi.nlm.nih.gov/pubmed/34809631 http://dx.doi.org/10.1186/s12911-021-01688-3 |