Why Bayes' Theorem Exposes the Accuracy Paradox in Machine Learning

Bayes' theorem reveals why accuracy alone is a deceptive metric in machine learning: high accuracy can coexist with catastrophically low precision when class imbalance exists. This Bayesian perspective—calculating posterior probability and understanding base rate fallacy—is especially critical in high-stakes domains like medical diagnosis, where seemingly accurate systems may miss the minority cases that matter most.