The central challenge of Machine learning models is perform well on unseen data. The data is randomly split into train and test set. The test set acts as surrogate for unseen data and is used to calculate generalized error.
However, discrete choice models (such as multinomial logit model) don’t require to split the data into train and test set. They are trained on whole dataset.
Can someone please explain why don’t discrete choice models need test set. It would be great if you can also cite reference so I can read in detail later.