which is better : F1-score of 'N' in imbalanced data or 'N+3' in balanced data?
Second.
why? you said you objective is to minimize misclassifications which equates to maximizing
F1 score, and you achieve that in inbalaced situations when you down-sample the majority class (one approach to maximize)
Potential Pitfall:
It could be that you just got rid of of some important infomration in the majority class and you are over-fitting on what is left.
In some cases 1500 datasamples would suffice, but I it could be that it doesn not have enough information. I would rather advise to upsample the smaller class, for example using ADASYN, SMOTe , knnSMOTE etc...
Only you can answer this question, by knowing some meta-information about your dataset, quality rules, are you expecting huge covariate-shift, is the dropped data really that different in distribution, etc...
Then do the analysis. If there is one thing you should always ask for is diverse and informative dataset, and more data will (almost) always beat better algorithm.