  The multiple-biomarker classifier problem and its assessment are reviewed
against the background of some fundamental principles from the field of
statistical pattern recognition, machine learning, or the recently so-called
"data science". A narrow reading of that literature has led many authors to
neglect the contribution to the total uncertainty of performance assessment
from the finite training sample. Yet the latter is a fundamental indicator of
the stability of a classifier; thus its neglect may be contributing to the
problematic status of many studies. A three-level strategy is proposed for
moving forward in this field. The lowest level is that of construction, where
candidate features are selected and the choice of classifier architecture is
made. At that point, the effective dimensionality of the classifier is
estimated and used to size the next level of analysis, a pilot study on
previously unseen cases. The total (training and testing) uncertainty resulting
from the pilot study is, in turn, used to size the highest level of analysis, a
pivotal study with a target level of uncertainty. Some resources available in
the literature for implementing this approach are reviewed. Although the
concepts explained in the present article may be fundamental and
straightforward for many researchers in the machine learning community they are
subtle for many practitioners, for whom we provided a general advice for the
best practice in \cite{Shi2010MAQCII} and elaborate here in the present paper.
