Why should stop an interactive machine learning algorithm as soon as the performance of the
model on a test set stops improving?
A.
To avoid the need for cross-validating the model
B.
To prevent overfitting
C.
To increase the VC (VAPNIK-Chervonenkis) dimension for the model
D.
To keep the number of terms in the model as possible
E.
To maintain the highest VC (Vapnik-Chervonenkis) dimension for the model