Undersampling approach in different types of study If I want to use an undersampling approach to construct the machine learning model, I am wondering if there are any criteria to determine how many times I should sample the data from the majority group (the minority is 14% and the majority is 86%) and build the ML model? I am working with biological data and I am recommended not to use oversampling approaches. To determine which sampling approach we should use, are there any criteria to determine before constructing the ML model? Is it really dependent on the field of study? 
 A: 
I am wondering if there are any criteria to determine how many times I
should sample the data from the majority group (the minority is 14%
and the majority is 86%) and build the ML model.

If you are interested in accuracy (or expected loss, which is just a sort of weighted accuracy), then I think the short answer is probably "no".  Class imbalance problems tend to arise when you have very little data, and so due to the imbalance, there are especially few patterns describing the distribution of the minority class.  This can lead to unduly biasing the decision surface towards the majority class.  However, the bias is generally fairly small.  If you do something as extreme as balancing the dataset so it has a 50:50 distribution, this is likely to way over-compensate, so that the true positive rate goes up, but the false negative rate goes up much faster and your accuracy goes down.  So basically you want to resample/reweight just enough to compensate for the bias, but no more, in order to improve test accuracy.  But if we have so little data that we have a class imbalance problem, we probably don't have enough data to estimate the required correction either (apart from simple methods like logistic regression), so in practice we may be better of doing nothing.
Fortunately, most parameter estimation problems go away if you collect enough new data, so that is often going to be the most practical solution.
