  In this paper, we address the probabilistic error quantification of a general
class of prediction methods. We consider a given prediction model and show how
to obtain, through a sample-based approach, a probabilistic upper bound on the
absolute value of the prediction error. The proposed scheme is based on a
probabilistic scaling methodology in which the number of required randomized
samples is independent of the complexity of the prediction model. The
methodology is extended to address the case in which the probabilistic
uncertain quantification is required to be valid for every member of a finite
family of predictors. We illustrate the results of the paper by means of a
numerical example.
