I am trying to run a regression supervised learning problem. The dataset is not very large and I wanted to do some cross-validation to avoid overfitting. As I have read it's important to do a sensitivity analysis to determine the value of k. Also, I would like to do some hyperparameter grid search for the algorithm (i.e. random forests). What would be the correct procedure? First take a random value of k and perform the hyperparameter grid search and with the correct hyperparameters do the sensitivity test for k or vice versa? Thanks in advance!

Full article content could not be extracted automatically. Read the original below.