I am wondering if there is a need to set the beta multiplier in Maxent (species distriubition modeling approach) if one is also reducing features using a contribution threshold. I have seen a number of published SDM papers that reduce the number of features, and I have seen others that combine feature reduction with a beta multiplier that is not the default.
I am using AUC to evaluate the models. In addition, I am using K fold validation to estimate a testing AUC. I am comparing the two AUCs to check for model overfitting.
My understanding of using a beta multiplier is basically to provide a "smoother" to compensate for the possibility of model overfitting. If I am removing features, do I also need to identify the "best" beta multiplier? If so, do I need to do this iteratively (i.e. at every subset of the original full feature dataset)?