Nyakora Bichang'a Simion

Calibration and Uncertainty Quantification for Machine Learning Models in Imbalanced HIV Viral Load Prediction: A Reliability-Based Evaluation

Machine learning (ML) models for HIV prediction are often evaluated using discrimination metrics (e.g., AUC) while neglecting calibration and predictive uncertainty, a critical gap in imbalanced clinical datasets where inaccurate probability estimates can harm decision-making. This study evaluated the calibration, unce...