While I've been looking for an explanation on how good adjusted r squared explains the data, I came across a term I havn't met before - Predicted R squared.

Apparantly, I've been living all this time in a lie, and neither R squared nor adjusted R squared are good measures for the fit of the model to the population. Supposedly, predicted r squared is the measure to turn to.

However, to my understanding, even predicted R squared isn't enough - you must compare it to R square / adjusted R square. If predicted R square is much lower than them, it means that the model is overfitting the data. In other words, should I test my model on a different sample, I might come up with entirely different results.

So my question is - How small is too small? Is there any rule of thumb to, lets say, the percentage of R square that predicted R square should be?

Thanks in advance