Why do discrete choice models (such as MNL) not require test set? 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.
Thanks

Multinomial logistic regression can and often does consider out-of-sample performance. For instance, LeCun (1998) applied multinomial logistic regression to the pixels of the MNIST handwritten digits, achieving an accuracy of $88\%$ on the test set.
As is discussed on the blog of our Demetri Pananos, biostatistics has a different take on model validation than out-of-sample performance, preferring to use bootstrap-based approaches, but that is not unique to models of discrete outcomes ("classification"), and there is an interesting debate about out-of-sample performance vs bootstrap (linked in the blog).
LeCun, Yann, et al. "Gradient-based learning applied to document recognition." Proceedings of the IEEE 86.11 (1998): 2278-2324.
