  Multivariate machine learning techniques provide an alternative to the
rapidity gap method for event-by-event identification and classification of
diffraction in hadron-hadron collisions. Traditionally, such methods assign
each event exclusively to a single class producing classification errors in
overlap regions of data space. As an alternative to this so called hard
classification approach, we propose estimating posterior probabilities of each
diffractive class and using these estimates to weigh event contributions to
physical observables. It is shown with a Monte Carlo study that such a soft
classification scheme is able to reproduce observables such as multiplicity
distributions and relative event rates with a much higher accuracy than hard
classification.
