Probabilistic metric spaces are a natural generalization of metric spaces in which the function that computes the distance outputs a distribution on the real numbers rather than a single number. Such a function is called a distribution function. In this paper, we construct a distance for linear regression models using one type of probabilistic metric space called F-space. F-spaces use fuzzy measures to evaluate a set of elements under certain conditions. By using F-spaces to build a metric on machine learning models, we permit to represent more complex interactions of the databases that generate these models.