  Novel prediction methods should always be compared to a baseline to know how
well they perform. Without this frame of reference, the performance score of a
model is basically meaningless. What does it mean when a model achieves an
$F_1$ of 0.8 on a test set? A proper baseline is needed to evaluate the
`goodness' of a performance score. Comparing with the latest state-of-the-art
model is usually insightful. However, being state-of-the-art can change rapidly
when newer models are developed. Contrary to an advanced model, a simple dummy
classifier could be used. However, the latter could be beaten too easily,
making the comparison less valuable. This paper presents a universal baseline
method for all binary classification models, named the Dutch Draw (DD). This
approach weighs simple classifiers and determines the best classifier to use as
a baseline. We theoretically derive the DD baseline for many commonly used
evaluation measures and show that in most situations it reduces to (almost)
always predicting either zero or one. Summarizing, the DD baseline is: (1)
general, as it is applicable to all binary classification problems; (2) simple,
as it is quickly determined without training or parameter-tuning; (3)
informative, as insightful conclusions can be drawn from the results. The DD
baseline serves two purposes. First, to enable comparisons across research
papers by this robust and universal baseline. Secondly, to provide a sanity
check during the development process of a prediction model. It is a major
warning sign when a model is outperformed by the DD baseline.
