# When should I oversample data?

I am dealing with multi-class classifiers. My data is unbalanced. Hence, I need to apply sampling techniques before training (undersampling or oversampling). When I apply undersampling, loss and val_loss, as well as acc and val_acc show a good fit. In this case, is it still necessary to oversample the data? What results should I expect?

• The trouble in a multiclass problem is that if you set a high standard of, say, $0.9$ probability of class membership, you might wind up with no class meeting your standard and having to consider that a grey zone. Frank Harrell would see this as a positive, however. // I have no idea how software handles this, though, and assume most standard packages just pick the class with the highest probability.