  Quantitative characterizations and estimations of uncertainty are of
fundamental importance in optimization and decision-making processes. Herein,
we propose intuitive scores, which we call certainty and doubt, that can be
used in both a Bayesian and frequentist framework to assess and compare the
quality and uncertainty of predictions in (multi-)classification decision
machine learning problems.
