f1 or accuracy scoring after downsampling - classification, svm - Python
If you've rebalanced your data, then it's not unbalanced anymore and I see no problem with using accuracy as the success metric.
Accuracy can mislead you in very skewed datasets but since it isn't skewed anymore, it should work.
I have a dataset consisting in 15 columns and 3000 rows to train a model for a binary classification. 
There is a imbalance for y (1:2). Both outcomes (0,1) are equally important.
After downsampling (because the parameter class_weight = balanced didn't work well), I used the parameter scoring = "f1", because I read that this was next to the ROC curve the best measurement of the performance. 
The question is: 
Do I treat my data after downsampling still as unbalanced and therefore apply f1 or can I go back to normal accuracy?
f1 = 2 * (precision * recall) / (precision + recall)
Cheers in advance! :)