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...
I tiakina i:
Kaituhi matua: | Aria, Massimo (auth) |
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Ētahi atu kaituhi: | Cuccurullo, Corrado (auth), Gnasso, Agostino (auth) |
Hōputu: | Tāhiko Wāhanga pukapuka |
Reo: | Ingarihi |
I whakaputaina: |
Florence
Firenze University Press
2021
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Rangatū: | Proceedings e report
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Ngā marau: | |
Urunga tuihono: | DOAB: download the publication DOAB: description of the publication |
Ngā Tūtohu: |
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Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
mā: Aria, Massimo
I whakaputaina: (2021) -
Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
mā: Aria, Massimo
I whakaputaina: (2021) -
Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
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I whakaputaina: (2021) -
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Chapter Effect of Climate Change and anthropogenic pressures on the European eel Anguilla anguilla from RAMSAR Wetland Ichkeul Lake: prediction from the Random Forest model
mā: Toujani, Rachid
I whakaputaina: (2022)