Estimating the Rashomon Ratio for Infinite Hypothesis Sets and Its Implications for Efficient Learning

The Rashomon ratio measures the proportion of classifiers in a family that yield a loss less than a given threshold. This work explores methods to estimate the Rashomon ratio for infinite hypothesis sets and demonstrates how a large Rashomon ratio can enable efficient learning by allowing good classifiers to be found through random sampling.