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Classification, the process of assigning a label (or class) to an observationgiven its features, is a common task in many applications. Nonetheless in mostreal-life applications, the labels can not be fully explained by the observedfeatures. Indeed there can be many factors hidden to the modellers. Theunexplained variation is then treated as some random noise which is handleddifferently depending on the method retained by the practitioner. This workfocuses on two simple and widely used supervised classification algorithms:discrete choice models and artificial neural networks in the context of binaryclassification.  Through various numerical experiments involving continuous or discreteexplanatory features, we present a comparison of the retained methods'performance in presence of missing variables. The impact of the distribution ofthe two classes in the training data is also investigated. The outcomes ofthose experiments highlight the fact that artificial neural networksoutperforms the discrete choice models, except when the distribution of theclasses in the training data is highly unbalanced.  Finally, this work provides some guidelines for choosing the right classifierwith respect to the training data.

Comparison of Discrete Choice Models and Artificial Neural Networks in Presence of Missing Variables