Depends entirely on what your model's properties and the performance measure are.
Lets take a simple example: suppose your model predicts all positives and half of the negatives correctly on your training set (e.g. accuracy 75%). Assuming the positives and negatives in the test set have similar distributions to those in the training set, the model will probably perform similarly on the test set. In that case the accuracy would drop to $(15+30)/75 = 60\%$.
You have to answer two questions before looking at performance numbers:
- What properties of the model matter to you?
- What score measure do you use to capture (1)?
You can eliminate the effect of unbalanced data by using score measures such as area under the ROC curve.