Im running an xgboost model to try and find important predictors for a disease from a list of almost 1000 covariates. The prevalence of the disease in my cohort is about 10%.
Given the imbalance data, would the precision-recall AUC or the logloss be a more appropriate matrix to assess the model fit? Is it appropriate to use logloss when classes are not balanced?
Also, playing with hyperparameters tuning, it seems like adding scale_pos_weight is benificial, but should i avoid doing this if i use logloss?