Splitting the data set into training and validation is a method to avoid overfitting. We can calculate metrics such as RMSE and R$2$ for the training and validation sets. Under what conditions, we can tell the model is overfitted?

Group of answer choices

Very low training error and high test error

Very high training and test errors

High training, but low testing error

Low training and testing errors