A Posteriori Corrections to Classification Methods

Abstract

A posteriori corrections are computational inexpensive and may improve accuracy , confidence, sensitivity or specificity of the model, or correct for the differences between a priori training and real (test) class distributions. Such corrections are applicable to neural and any other classification models.