Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models

Variable selection (VS) is a critical step in developing predictive binary classification (BC) models. Many traditional methods for assessing the added value of a candidate variable provide global performance summaries and lack an interpretable graphical summary of results. To address this limitatio...

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Bibliographic Details
Main Authors:	Katarzyna B. Kubiak, Agata Konieczna, Anna Tyranska-Fobke, Barbara Więckowska
Format:	Article
Language:	English
Published:	MDPI AG 2025-07-01
Series:	Applied Sciences
Subjects:	U-smile method class imbalance variable selection class-stratified assessment explainable machine learning explainable artificial intelligence
Online Access:	https://www.mdpi.com/2076-3417/15/15/8303
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Beyond Global Metrics: The U-Smile Method for Explainable, Interpretable, and Transparent Variable Selection in Risk Prediction Models

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