23.5.3 Model Uncertainty
Model Uncertainty-based methods select training points for the purpose of reducing uncertainty within the model, more specifically, to reduce uncertainty about
the model’s parameters. The assumption is that if we improve the accuracy of the
model’s parameters the accuracy of output values will improve as well. If we were to
predict a user’s preferences based on membership in different interest groups [23],
i.e. a group of people with a similar interest, then training points may be selected so
as to determine to which groups the user belongs (Section 23.5.3.1).