  Since its inception, the choice modelling field has been dominated by
theory-driven modelling approaches. Machine learning offers an alternative
data-driven approach for modelling choice behaviour and is increasingly drawing
interest in our field. Cross-pollination of machine learning models, techniques
and practices could help overcome problems and limitations encountered in the
current theory-driven modelling paradigm, such as subjective labour-intensive
search processes for model selection, and the inability to work with text and
image data. However, despite the potential benefits of using the advances of
machine learning to improve choice modelling practices, the choice modelling
field has been hesitant to embrace machine learning. This discussion paper aims
to consolidate knowledge on the use of machine learning models, techniques and
practices for choice modelling, and discuss their potential. Thereby, we hope
not only to make the case that further integration of machine learning in
choice modelling is beneficial, but also to further facilitate it. To this end,
we clarify the similarities and differences between the two modelling
paradigms; we review the use of machine learning for choice modelling; and we
explore areas of opportunities for embracing machine learning models and
techniques to improve our practices. To conclude this discussion paper, we put
forward a set of research questions which must be addressed to better
understand if and how machine learning can benefit choice modelling.
