The paper carries out a comparative study that is based on the use of credibility theory to examine pentagonal and hexagonal fuzzy membership functions of machine learning systems. These fuzzy memberships can be used to manage the uncertainty and imprecision of a data driven-model which allows better decision-making in the case of vague or incomplete information. The credibility theory is used to determine quantitatively the reliability of the inferences obtained through each function. Both the membership functions are modelled, incorporated in machine learning framework and tested on randomly generated as well as application specific datasets. The results obtained indicate that the performa... More >