which is better : F1-score of 'N' in imbalanced data or 'N+3' in balanced data? I have a dataset with 4712 records. Label 1 is 1558(33%) and Label 0 is 3154 (67%)

a)  Currently when I run the model and analysis as is (without sampling techniques), I get an F1-score of 71-77. I chose F1-score and AUC score as the metric as my dataset is imbalanced. (Atleast that's what I felt looking at class proportion). My AUC also ranges between 80-83 for tree based models. Screenshot of models with imbalanced data is given below



b) When I under-sample the majority class, I get all the metrics like F1-score/accuracy/AUC above 80 but less than 85

screenshot below



Now I am not sure which one should I consider? I know it's all about trade-offs.

My objective is to avoid/minimize the misclassifications

Based on your experience in ML projects, what would you guys suggest?

Can someone enlighten me with some reasons why to choose one over the other?
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.