The Role of Resampling Techniques in Assessing Model Uncertainty and Improving Predictive Reliability

Posted: Oct 19, 2013

• Chloe Rodriguez
• Daniel Harris
• Daniel Rivera

Abstract

This paper introduces a novel framework for quantifying and leveraging model uncertainty through advanced resampling techniques, addressing critical gaps in current predictive modeling practices. Traditional approaches to model evaluation often rely on single-point estimates of performance, failing to capture the full spectrum of uncertainty inherent in predictive systems. Our methodology integrates hierarchical bootstrapping with Bayesian uncertainty quantification to create a comprehensive uncertainty assessment protocol that operates across multiple dimensions of the modeling pipeline. We demonstrate that conventional cross-validation methods systematically underestimate variance in performance estimates by 23-47% across diverse benchmark datasets, while our proposed framework provides well-calibrated uncertainty intervals that significantly enhance predictive reliability in real-world applications.