I am currently using auto gluon to train my dataset. When looking at the feature importance value of WeightedEnsembles_L2, I realise that the importance number is larger than the validation or the test score. Is this normal??
From my understanding, a score of x means that when the feature’s value was randomly shuffled across rows, the model’s performance dropped by x. If x is larger than R squared, does that make sense anymore?
Thanks in advance!
I tried to search up info regarding to this and found no result.
