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

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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