Interpretation of model

Question

I have two models $Y$ ~ $X_1 + X_5$ and $Y$ ~ $X_2 + X_4$ ($Y$ is Binary). Both models produce different coefficients using training data, predicted probability using testing data and ROC curve using testing data.

But they give same AUC value. How can I define or interpret this?

Answer 1

It seems your models are pretty much equally efficient, so maybe all four features are relevant. Have you tried a model that incorportaes all of them?