  Classification, the process of assigning a label (or class) to an observation
given its features, is a common task in many applications. Nonetheless in most
real-life applications, the labels can not be fully explained by the observed
features. Indeed there can be many factors hidden to the modellers. The
unexplained variation is then treated as some random noise which is handled
differently depending on the method retained by the practitioner. This work
focuses on two simple and widely used supervised classification algorithms:
discrete choice models and artificial neural networks in the context of binary
classification.
  Through various numerical experiments involving continuous or discrete
explanatory features, we present a comparison of the retained methods'
performance in presence of missing variables. The impact of the distribution of
the two classes in the training data is also investigated. The outcomes of
those experiments highlight the fact that artificial neural networks
outperforms the discrete choice models, except when the distribution of the
classes in the training data is highly unbalanced.
  Finally, this work provides some guidelines for choosing the right classifier
with respect to the training data.
