Speaker
Description
Background: Traditional binary classification assessment in machine learning relies heavily on decision thresholds, limiting interpretability and performance in imbalanced scenarios. While metrics like AUC under ROC (Receiver Operating Characteristic curve) provide overall performance measures, they fail to deliver class-specific insights, which is crucial for real-world applications with uneven class distributions.
Methods: We introduce the U-smile method [1, 2] a novel machine learning framework featuring threshold-free visualization that decomposes the relative likelihood ratio (rLR) into event and non-event components displayed through characteristic U-shaped plots. Comprehensive experiments employed six synthetic datasets with varying predictive power and class imbalance ratios (balanced to 90/10). The method was compared against AUC-based variable selection using bidirectional stepwise selection with 10-fold cross-validation.
Results: In severely imbalanced scenarios (90/10 distribution), U-smile methods demonstrated superior performance, selecting more relevant variables (4-5 vs 3) and achieving substantial improvements in minority class detection. Key metrics showed significant enhancement: AUC-PR (area under Precision-Recall curve) increased by 16% (0.701→0.812) and F1-score by 21% (0.662→0.798). The framework adheres to Explainable Machine Learning (EML) principles by providing intuitive graphical assessment tools. Evolutionary analysis of U-smile patterns revealed progressive symmetry achievement, with the non-event variant attaining near-optimal balance (rLR₀=0.535, rLR₁=0.569) despite extreme class imbalance.
Conclusion: The U-smile framework offers a threshold-free, class-specific evaluation approach that outperforms conventional AUC-based methods in imbalanced classification. Its visual interpretability and capacity to identify minority-class-beneficial variables make it particularly valuable for practical applications where class imbalance is prevalent, while simultaneously advancing explainable AI through transparent model assessment.
[1] Więckowska B, Kubiak KB, Guzik P. Evaluating the three-level approach of the U-smile method for imbalanced binary classification. PLOS ONE 2025;20:e0321661
[2] Kubiak KB, Więckowska B, Jodłowska-Siewert E, Guzik P. Visualising and quantifying the usefulness of new predictors stratified by outcome class: The U-smile method. Plos One 2024;19:e0303276
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