  Before any binary classification model is taken into practice, it is
important to validate its performance on a proper test set. Without a frame of
reference given by a baseline method, it is impossible to determine if a score
is `good' or `bad'. The goal of this paper is to examine all baseline methods
that are independent of feature values and determine which model is the `best'
and why. By identifying which baseline models are optimal, a crucial selection
decision in the evaluation process is simplified. We prove that the recently
proposed Dutch Draw baseline is the best input-independent classifier
(independent of feature values) for all positional-invariant measures
(independent of sequence order) assuming that the samples are randomly
shuffled. This means that the Dutch Draw baseline is the optimal baseline under
these intuitive requirements and should therefore be used in practice.
