Before revision:  This suggests that standard machine learning techniques based on random forests can serve to estimate discrete choice models with an interpretable output . This is confirmed by our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently , with its splitting criterion capable of recovering preference rank lists .
After revision:  This suggests that standard machine learning techniques based on random forests can serve to estimate discrete choice models with an interpretable output : the underlying trees can be viewed as the internal choice process of customers. Our data-driven theoretical results which show that random forests can predict the choice probability of any discrete choice model consistently , with its splitting criterion capable of recovering preference rank lists .