shallow, yet wide, and nested data problems
nested transactional data = all the claims for a person for example.
Considering a database of retail purchases that includes the item bought, the purchaser, and the date and time of purchase, it's easy to construct a model that will fit the training set perfectly by using the date and time of purchase to predict the other attributes; but this model will not generalize at all to new data, because those past times will never occur again.
Information from all past experience can be divided into two groups:
- information that is relevant for the future
- information that is irrelevant (noise).