Class balancing of the dataset
As I have mentioned in the comment, there is not rule of thumb. One has to do a lot experimentation. 

Based on how much data you have, bringing minority class to be be equal (50-50) to the majority class can be misleading the model. 

You can also try ADASYN during your experimentation. 

ADASYN is a improved version of SMOTE. What it does is same as SMOTE just with a minor improvement. After creating those sample it adds a random small values to the points thus making it more realistic. In other words instead of all the sample being linearly correlated to the parent they have a little more variance in them i.e they are bit scattered.

You can read about the implementation here