# 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

• Can you be more specific on what you mean by discrete choice models? May 11 at 9:28
• Such as binary logit model, multinomial logit model
– SiH
May 11 at 12:44
• IS MNL "multinomial logistic regression"?
– Dave
May 11 at 12:44
• Yes that is correct
– SiH
May 11 at 12:46
• From what I have previously discussed with some colleagues, discrete choice is more theory driven than data driven. often there is a lot of internal validation, taking subsets of the data to show validity, extrapolating data from the population using surrogate measures to make comparisons, previous studies etc. . But I also see there is a lot of pushback on this. see this sciencedirect.com/science/article/pii/S1755534520300543 May 11 at 13:24

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.