Concerning these two goals, various procedures are
proposed:
Resubstitution Validation
In resubstitution validation, the model is learned from
all the available data and then tested on the same set of
data. This validation process uses all the available data
but suffers seriously from over-fitting. That is, the
algorithm might perform well on the available data
yet poorly on future unseen test data.