  This paper focuses on optimal unimodal transformation of the score outputs of
a univariate learning model under linear loss functions. We demonstrate that
the optimal mapping between score values and the target region is a rectangular
function. To produce this optimal rectangular fit for the observed samples, we
propose a sequential approach that can its estimation with each incoming new
sample. Our approach has logarithmic time complexity per iteration and is
optimally efficient.
