My dataset is approxiately balanced: 52/48. I evaluate both ACC and F1-score. The result returned by Random forest model is below
Acc: 52%
F1: 68%
Confusion matrix:
|Predicted
Label|0 |1
0 |52|122109
1 |19|134802
I know if I switch labels 0 as 1 and vice versa, the F1 score will be very small. So, in the case of using F1, should I always switch labels?
The interpretation of F1 score entirely depends on an arbitrary choice of labels (this is buried within its formulation). F1 score is therefore most suitable for cases where class labels actually mean and correspond to negative and positive in real-life (e.g., presence of cancer) and where there is an imbalanced class distribution (particularly, when the negatives significantly outnumber the positives). Since your data is balanced and it seems you can arbitrarily switch the labels too, F1 score may not be a suitable metric to use.
