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Alternative stopping rules to limit tree expansion for random forest models
Random forests are a popular type of machine learning model, which are relatively robust to overfitting, unlike some other machine learning models, and adequately capture non-linear relationships between an outcome of interest and multiple independent variables. There are relatively few adjustable h...
Autores principales: | Little, Mark P., Rosenberg, Philip S., Arsham, Aryana |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9448733/ https://www.ncbi.nlm.nih.gov/pubmed/36068261 http://dx.doi.org/10.1038/s41598-022-19281-7 |
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