# parameter tuning using nested cross validation

Parameter tuning in SVM has been performed using a nested cross-validation(CV) approach with 45 folds(outer loop) and 13 folds(inner loop). In this process, the outer loop will have 45 prediction tasks with a chosen best value for C from the inner loop. I have taken the values 0.5, 5, 50, 500 for tuning the parameter C and observed that F1-score was highest 34 times for value 5, 6 times for value 50, 5 times for value 500.

Would it be a good idea to test the model on an external dataset(not involved in nested CV) with value 5 for C(highest frequent best value) ?

PS: as you are only tuning C I'm assuming you are using a linear kernel?