How does Naïve Bayes behave under probabilistic calibration constraints in regulated systems?

In regulated systems, Naïve Bayes often requires post-hoc calibration to ensure probability outputs reflect real-world frequencies.

Regulated domains like finance and healthcare require calibrated probabilities. Naïve Bayes tends to produce overconfident predictions due to independence assumption. Calibration methods such as Platt scaling, isotonic regression, or temperature scaling are applied after training to align predicted probabilities with observed frequencies.