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...
Gorde:
| Egile nagusia: | |
|---|---|
| Beste egile batzuk: | , |
| Formatua: | Baliabide elektronikoa Liburu kapitulua |
| Hizkuntza: | ingelesa |
| Argitaratua: |
Florence
Firenze University Press
2021
|
| Saila: | Proceedings e report
|
| Gaiak: | |
| Sarrera elektronikoa: | DOAB: download the publication DOAB: description of the publication |
| Etiketak: |
Etiketa erantsi
Etiketarik gabe, Izan zaitez lehena erregistro honi etiketa jartzen!
|
Search Result 1
OAPEN Library: description of the publication
Chapter Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests
Argitaratua 2021
OAPEN Library: download the publication
OAPEN Library: description of the publication
Baliabide elektronikoa
Liburu kapitulua
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
Argitaratua 2021
OAPEN Library: download the publication
OAPEN Library: description of the publication
Baliabide elektronikoa
Liburu kapitulua
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
Argitaratua 2021
DOAB: download the publication
DOAB: description of the publication
Baliabide elektronikoa
Liburu kapitulua