Model Selection using Bias Variance Trade Off I have a Regression Model with Train MAPE as 6% and Test MAPE as 15%.
This appears to me as a clear case of over fitting. But can I still use this model assuming 15% Error is not a bad number after-all. Is this there a flaw in this thinking?
Yes, assuming you haven't overfitted on the test set (which may happen after extensive hyperparameter optimization), you can assume that your model has a MAPE of 15%.
However, if you limit the overfitting, the test performance would probably go down!