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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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Principais autores: Uraquitan Lima Filho, Tiago Alexandre Pais, Ricardo Jorge Pais
Format: Artigo
Sprog:Inglês
Udgivet: MDPI AG 2025-09-01
Serier:BioMedInformatics
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Online adgang:https://www.mdpi.com/2673-7426/5/3/55
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