Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not...
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| Formato: | Recurso Eletrônico Capítulo de Livro |
| Idioma: | inglês |
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Florence
Firenze University Press
2021
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| coleção: | Proceedings e report
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| Acesso em linha: | DOAB: download the publication DOAB: description of the publication |
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OAPEN Library: description of the publication
Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
Publicado em 2021
OAPEN Library: download the publication
OAPEN Library: description of the publication
Recurso Eletrônico
Capítulo de Livro
Search Result 2
OAPEN Library: description of the publication
Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
Publicado em 2021
OAPEN Library: download the publication
OAPEN Library: description of the publication
Recurso Eletrônico
Capítulo de Livro
Search Result 3
DOAB: description of the publication
Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
Publicado em 2021
DOAB: download the publication
DOAB: description of the publication
Recurso Eletrônico
Capítulo de Livro