New Preprint Under Review: The Divergence of Classification Metrics
- 2 hours ago
- 1 min read
I am pleased to share that the preprint for my latest academic paper, "Minimal counterexamples separating accuracy, proper scoring rules, and calibration error in probabilistic classification," is now publicly available on the Social Science Research Network (SSRN). As part of the publication process, the journal currently reviewing the manuscript has issued this preprint.
In algorithmic decision-making and probabilistic classification, we often assume that optimizing for accuracy naturally improves both calibration and proper scoring. But does it always? This paper explores the mathematical boundaries of model evaluation, presenting minimal counterexamples that demonstrate exactly how these three core metrics can fundamentally diverge.
This research highlights the critical need to carefully select evaluation frameworks rather than relying on a single metric, ensuring algorithms actually behave as intended in real-world applications.
While the manuscript is undergoing formal peer review, I welcome early thoughts and feedback from the academic and data science communities.
Read the preprint on SSRN here: http://dx.doi.org/10.2139/ssrn.7082155





