Visualising the Truth: A Composite Evaluation Framework for Score-Based Predictive Model Selection
<b>Background:</b> The selection of machine learning (ML) models in the biomedical sciences often relies on global performance metrics. When these metrics are closely clustered among candidate models, identifying the most suitable model for real-world deployment becomes challenging. <b>Methods:</b>...
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| Autori principali: | , , |
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| Natura: | Artigo |
| Lingua: | Inglês |
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MDPI AG
2025-09-01
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| Serie: | BioMedInformatics |
| Soggetti: | |
| Accesso online: | https://www.mdpi.com/2673-7426/5/3/55 |
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