When dealing with unbalanced class, which is better,
- oversampling/undersampling of the classes or
- randomly selecting equal number of positive samples and negative samples from the training dataset and combining as training samples, transforming the imbalanced classification problem and replacing by multiple balanced data classification problems?
Does one of them have an advantage over the other? If so, which one? I am asking for a generalized point of view. If you had an unbalanced dataset, which option would you choose, 1 or 2?