Cargando…
Predicting and Mitigating Freshmen Student Attrition: A Local-Explainable Machine Learning Framework
With the emergence of novel methods for improving machine learning (ML) transparency, traditional decision-support-focused information systems seem to need an upgrade in their approach toward providing more actionable insights for practitioners. Particularly, given the complex decision-making proces...
Autores principales: | Delen, Dursun, Davazdahemami, Behrooz, Rasouli Dezfouli, Elham |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Springer US
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10097523/ https://www.ncbi.nlm.nih.gov/pubmed/37361887 http://dx.doi.org/10.1007/s10796-023-10397-3 |
Ejemplares similares
-
An explanatory machine learning framework for studying pandemics: The case of COVID-19 emergency department readmissions
por: Davazdahemami, Behrooz, et al.
Publicado: (2022) -
An explanatory analytics framework for early detection of chronic risk factors in pandemics
por: Davazdahemami, Behrooz, et al.
Publicado: (2022) -
No Place Like Home: Cross-National Data Analysis of the Efficacy of Social Distancing During the COVID-19 Pandemic
por: Delen, Dursun, et al.
Publicado: (2020) -
A Bayesian belief network-based analytics methodology for early-stage risk detection of novel diseases
por: Topuz, Kazim, et al.
Publicado: (2023) -
A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19
por: Eryarsoy, Enes, et al.
Publicado: (2021)