Data Under fitting and its handling Regularization is used to handle over-fitting problem.

Similarly can we have some methodology to overcome under fitting problem or merely adding new features or training data will help us in reducing under-fitting issue?
As such we don't have much ways to handle under-fitting issue.
Generally I follow following ways to handle it:


Add more features to training data. That includes deriving new features front the existing one.
Make a complex model.
Increase iterations.


Adding new training data only, I believe will not solve this problem.